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• Published: 29 October 2020

Urban and air pollution: a multi-city study of long-term effects of urban landscape patterns on air quality trends

• Lu Liang 1 &
• Peng Gong 2 , 3 , 4  

Scientific Reports volume  10 , Article number:  18618 ( 2020 ) Cite this article

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Most air pollution research has focused on assessing the urban landscape effects of pollutants in megacities, little is known about their associations in small- to mid-sized cities. Considering that the biggest urban growth is projected to occur in these smaller-scale cities, this empirical study identifies the key urban form determinants of decadal-long fine particulate matter (PM 2.5 ) trends in all 626 Chinese cities at the county level and above. As the first study of its kind, this study comprehensively examines the urban form effects on air quality in cities of different population sizes, at different development levels, and in different spatial-autocorrelation positions. Results demonstrate that the urban form evolution has long-term effects on PM 2.5 level, but the dominant factors shift over the urbanization stages: area metrics play a role in PM 2.5 trends of small-sized cities at the early urban development stage, whereas aggregation metrics determine such trends mostly in mid-sized cities. For large cities exhibiting a higher degree of urbanization, the spatial connectedness of urban patches is positively associated with long-term PM 2.5 level increases. We suggest that, depending on the city’s developmental stage, different aspects of the urban form should be emphasized to achieve long-term clean air goals.

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Introduction

Air pollution represents a prominent threat to global society by causing cascading effects on individuals 1 , medical systems 2 , ecosystem health 3 , and economies 4 in both developing and developed countries 5 , 6 , 7 , 8 . About 90% of global citizens lived in areas that exceed the safe level in the World Health Organization (WHO) air quality guidelines 9 . Among all types of ecosystems, urban produce roughly 78% of carbon emissions and substantial airborne pollutants that adversely affect over 50% of the world’s population living in them 5 , 10 . While air pollution affects all regions, there exhibits substantial regional variation in air pollution levels 11 . For instance, the annual mean concentration of fine particulate matter with an aerodynamic diameter of less than 2.5  \(\upmu\mathrm{m}\) (PM 2.5 ) in the most polluted cities is nearly 20 times higher than the cleanest city according to a survey of 499 global cities 12 . Many factors can influence the regional air quality, including emissions, meteorology, and physicochemical transformations. Another non-negligible driver is urbanization—a process that alters the size, structure, and growth of cities in response to the population explosion and further leads to lasting air quality challenges 13 , 14 , 15 .

With the global trend of urbanization 16 , the spatial composition, configuration, and density of urban land uses (refer to as urban form) will continue to evolve 13 . The investigation of urban form impacts on air quality has been emerging in both empirical 17 and theoretical 18 research. While the area and density of artificial surface areas have well documented positive relationship with air pollution 19 , 20 , 21 , the effects of urban fragmentation on air quality have been controversial. In theory, compact cities promote high residential density with mixed land uses and thus reduce auto dependence and increase the usage of public transit and walking 21 , 22 . The compact urban development has been proved effective in mitigating air pollution in some cities 23 , 24 . A survey of 83 global urban areas also found that those with highly contiguous built-up areas emitted less NO 2 22 . In contrast, dispersed urban form can decentralize industrial polluters, improve fuel efficiency with less traffic congestion, and alleviate street canyon effects 25 , 26 , 27 , 28 . Polycentric and dispersed cities support the decentralization of jobs that lead to less pollution emission than compact and monocentric cities 29 . The more open spaces in a dispersed city support air dilution 30 . In contrast, compact cities are typically associated with stronger urban heat island effects 31 , which influence the availability and the advection of primary and secondary pollutants 32 .

The mixed evidence demonstrates the complex interplay between urban form and air pollution, which further implies that the inconsistent relationship may exist in cities at different urbanization levels and over different periods 33 . Few studies have attempted to investigate the urban form–air pollution relationship with cross-sectional and time series data 34 , 35 , 36 , 37 . Most studies were conducted in one city or metropolitan region 38 , 39 or even at the country level 40 . Furthermore, large cities or metropolitan areas draw the most attention in relevant studies 5 , 41 , 42 , and the small- and mid-sized cities, especially those in developing countries, are heavily underemphasized. However, virtually all world population growth 43 , 44 and most global economic growth 45 , 46 are expected to occur in those cities over the next several decades. Thus, an overlooked yet essential task is to account for various levels of cities, ranging from large metropolitan areas to less extensive urban area, in the analysis.

This study aims to improve the understanding of how the urban form evolution explains the decadal-long changes of the annual mean PM 2.5 concentrations in 626 cities at the county-level and above in China. China has undergone unprecedented urbanization over the past few decades and manifested a high degree of heterogeneity in urban development 47 . Thus, Chinese cities serve as a good model for addressing the following questions: (1) whether the changes in urban landscape patterns affect trends in PM 2.5 levels? And (2) if so, do the determinants vary by cities?

City boundaries

Our study period spans from the year 2000 to 2014 to keep the data completeness among all data sources. After excluding cities with invalid or missing PM 2.5 or sociodemographic value, a total of 626 cities, with 278 prefecture-level cities and 348 county-level cities, were selected. City boundaries are primarily based on the Global Rural–Urban Mapping Project (GRUMP) urban extent polygons that were defined by the extent of the nighttime lights 48 , 49 . Few adjustments were made. First, in the GRUMP dataset, large agglomerations that include several cities were often described in one big polygon. We manually split those polygons into individual cities based on the China Administrative Regions GIS Data at 1:1 million scales 50 . Second, since the 1978 economic reforms, China has significantly restructured its urban administrative/spatial system. Noticeable changes are the abolishment of several prefectures and the promotion of many former county-level cities to prefecture-level cities 51 . Thus, all city names were cross-checked between the year 2000 and 2014, and the mismatched records were replaced with the latest names.

PM 2.5 concentration data

The annual mean PM 2.5 surface concentration (micrograms per cubic meter) for each city over the study period was calculated from the Global Annual PM 2.5 Grids at 0.01° resolution 52 . This data set combines Aerosol Optical Depth retrievals from multiple satellite instruments including the NASA Moderate Resolution Imaging Spectroradiometer (MODIS), Multi-angle Imaging SpectroRadiometer (MISR), and the Sea-Viewing Wide Field-of-View Sensor (SeaWiFS). The global 3-D chemical transport model GEOS-Chem is further applied to relate this total column measure of aerosol to near-surface PM 2.5 concentration, and geographically weighted regression is finally used with global ground-based measurements to predict and adjust for the residual PM 2.5 bias per grid cell in the initial satellite-derived values.

Human settlement layer

The urban forms were quantified with the 40-year (1978–2017) record of annual impervious surface maps for both rural and urban areas in China 47 , 53 . This state-of-art product provides substantial spatial–temporal details on China’s human settlement changes. The annual impervious surface maps covering our study period were generated from 30-m resolution Landsat images acquired onboard Landsat 5, 7, and 8 using an automatic “Exclusion/Inclusion” mapping framework 54 , 55 . The output used here was the binary impervious surface mask, with the value of one indicating the presence of human settlement and the value of zero identifying non-residential areas. The product assessment concluded good performance. The cross-comparison against 2356 city or town locations in GeoNames proved an overall high agreement (88%) and approximately 80% agreement was achieved when compared against visually interpreted 650 urban extent areas in the year 1990, 2000, and 2010.

Control variables

To provide a holistic assessment of the urban form effects, we included control variables that are regarded as important in influencing air quality to account for the confounding effects.

Four variables, separately population size, population density, and two economic measures, were acquired from the China City Statistical Yearbook 56 (National Bureau of Statistics 2000–2014). Population size is used to control for the absolute level of pollution emissions 41 . Larger populations are associated with increased vehicle usage and vehicle-kilometers travels, and consequently boost tailpipes emissions 5 . Population density is a useful reflector of transportation demand and the fraction of emissions inhaled by people 57 . We also included gross regional product (GRP) and the proportion of GRP generated from the secondary sector (GRP2). The impact of economic development on air quality is significant but in a dynamic way 58 . The rising per capita income due to the concentration of manufacturing industrial activities can deteriorate air quality and vice versa if the stronger economy is the outcome of the concentration of less polluting high-tech industries. Meteorological conditions also have short- and long-term effects on the occurrence, transport, and dispersion of air pollutants 59 , 60 , 61 . Temperature affects chemical reactions and atmospheric turbulence that determine the formation and diffusion of particles 62 . Low air humidity can lead to the accumulation of air pollutants due to it is conducive to the adhesion of atmospheric particulate matter on water vapor 63 . Whereas high humidity can lead to wet deposition processes that can remove air pollutants by rainfall. Wind speed is a crucial indicator of atmospheric activity by greatly affect air pollutant transport and dispersion. All meteorological variables were calculated based on China 1 km raster layers of monthly relative humidity, temperature, and wind speed that are interpolated from over 800 ground monitoring stations 64 . Based on the monthly layer, we calculated the annual mean of each variable for each year. Finally, all pixels falling inside of the city boundary were averaged to represent the overall meteorological condition of each city.

Considering the dynamic urban form-air pollution relationship evidenced from the literature review, our hypothesis is: the determinants of PM 2.5 level trends are not the same for cities undergoing different levels of development or in different geographic regions. To test this hypothesis, we first categorized city groups following (1) social-economic development level, (2) spatial autocorrelation relationship, and (3) population size. We then assessed the relationship between urban form and PM 2.5 level trends by city groups. Finally, we applied the panel data models to different city groups for hypothesis testing and key determinant identification (Fig.  1 ).

Methodology workflow.

Calculation of urban form metrics

Based on the previous knowledge 65 , 66 , 67 , fifteen landscape metrics falling into three categories, separately area, shape, and aggregation, were selected. Those metrics quantify the compositional and configurational characteristics of the urban landscape, as represented by urban expansion, urban shape complexity, and compactness (Table 1 ).

Area metrics gives an overview of the urban extent and the size of urban patches that are correlated with PM 2.5 20 . As an indicator of the urbanization degree, total area (TA) typically increases constantly or remains stable, because the urbanization process is irreversible. Number of patches (NP) refers to the number of discrete parcels of urban settlement within a given urban extent and Mean Patch Size (AREA_MN) measures the average patch size. Patch density (PD) indicates the urbanization stages. It usually increases with urban diffusion until coalescence starts, after which decreases in number 66 . Largest Patch Index (LPI) measures the percentage of the landscape encompassed by the largest urban patch.

The shape complexity of urban patches was represented by Mean Patch Shape Index (SHAPE_MN), Mean Patch Fractal Dimension (FRAC_MN), and Mean Contiguity Index (CONTIG_MN). The greater irregularity the landscape shape, the larger the value of SHAPE_MN and FRAC_MN. CONTIG_MN is another method of assessing patch shape based on the spatial connectedness or contiguity of cells within a patch. Larger contiguous patches will result in larger CONTIG_MN.

Aggregation metrics measure the spatial compactness of urban land, which affects pollutant diffusion and dilution. Mean Euclidean nearest-neighbor distance (ENN_MN) quantifies the average distance between two patches within a landscape. It decreases as patches grow together and increases as the urban areas expand. Landscape Shape Index (LSI) indicates the divergence of the shape of a landscape patch that increases as the landscape becomes increasingly disaggregated 68 . Patch Cohesion Index (COHESION) is suggestive of the connectedness degree of patches 69 . Splitting Index (SPLIT) and Landscape Division Index (DIVISION) increase as the separation of urban patches rises, whereas, Mesh Size (MESH) decreases as the landscape becomes more fragmented. Aggregation Index (AI) measures the degree of aggregation or clumping of urban patches. Higher values of continuity indicate higher building densities, which may have a stronger effect on pollution diffusion.

The detailed descriptions of these indices are given by the FRAGSTATS user’s guide 70 . The calculation input is a layer of binary grids of urban/nonurban. The resulting output is a table containing one row for each city and multiple columns representing the individual metrics.

Division of cities

Division based on the socioeconomic development level.

The socioeconomic development level in China is uneven. The unequal development of the transportation system, descending in topography from the west to the east, combined with variations in the availability of natural and human resources and industrial infrastructure, has produced significantly wide gaps in the regional economies of China. By taking both the economic development level and natural geography into account, China can be loosely classified into Eastern, Central, and Western regions. Eastern China is generally wealthier than the interior, resulting from closeness to coastlines and the Open-Door Policy favoring coastal regions. Western China is historically behind in economic development because of its high elevation and rugged topography, which creates barriers in the transportation infrastructure construction and scarcity of arable lands. Central China, echoing its name, is in the process of economic development. This region neither benefited from geographic convenience to the coast nor benefited from any preferential policies, such as the Western Development Campaign.

Division based on spatial autocorrelation relationship

The second type of division follows the fact that adjacent cities are likely to form air pollution clusters due to the mixing and diluting nature of air pollutants 71 , i.e., cities share similar pollution levels as its neighbors. The underlying processes driving the formation of pollution hot spots and cold spots may differ. Thus, we further divided the city into groups based on the spatial clusters of PM 2.5 level changes.

Local indicators of spatial autocorrelation (LISA) was used to determine the local patterns of PM 2.5 distribution by clustering cities with a significant association. In the presence of global spatial autocorrelation, LISA indicates whether a variable exhibits significant spatial dependence and heterogeneity at a given scale 72 . Practically, LISA relates each observation to its neighbors and assigns a value of significance level and degree of spatial autocorrelation, which is calculated by the similarity in variable \(z\) between observation \(i\) and observation \(j\) in the neighborhood of \(i\) defined by a matrix of weights \({w}_{ij}\) 7 , 73 :

where \({I}_{i}\) is the Moran’s I value for location \(i\) ; \({\sigma }^{2}\) is the variance of variable \(z\) ; \(\bar{z}\) is the average value of \(z\) with the sample number of \(n\) . The weight matrix \({w}_{ij}\) is defined by the k-nearest neighbors distance measure, i.e., each object’s neighborhood consists of four closest cites.

The computation of Moran’s I enables the identification of hot spots and cold spots. The hot spots are high-high clusters where the increase in the PM 2.5 level is higher than the surrounding areas, whereas cold spots are low-low clusters with the presence of low values in a low-value neighborhood. A Moran scatterplot, with x-axis as the original variable and y-axis as the spatially lagged variable, reflects the spatial association pattern. The slope of the linear fit to the scatter plot is an estimation of the global Moran's I 72 (Fig.  2 ). The plot consists of four quadrants, each defining the relationship between an observation 74 . The upper right quadrant indicates hot spots and the lower left quadrant displays cold spots 75 .

Moran’s I scatterplot. Figure was produced by R 3.4.3 76 .

Division based on population size

The last division was based on population size, which is a proven factor in changing per capita emissions in a wide selection of global cities, even outperformed land urbanization rate 77 , 78 , 79 . We used the 2014 urban population to classify the cities into four groups based on United Nations definitions 80 : (1) large agglomerations with a total population larger than 1 million; (2) mid-sized cities, 500,000–1 million; (3) small cities, 250,000–500,000, and (4) very small cities, 100,000–250,000.

Panel data analysis

The panel data analysis is an analytical method that deals with observations from multiple entities over multiple periods. Its capacity in analyzing the characteristics and changes from both the time-series and cross-section dimensions of data surpasses conventional models that purely focus on one dimension 81 , 82 . The estimation equation for the panel data model in this study is given as:

where the subscript \(i\) and \(t\) refer to city and year respectively. \(\upbeta _{{0}}\) is the intercept parameter and \(\upbeta _{{1}} - { }\upbeta _{{{18}}}\) are the estimates of slope coefficients. \(\varepsilon \) is the random error. All variables are transformed into natural logarithms.

Two methods can be used to obtain model estimates, separately fixed effects estimator and random effects estimator. The fixed effects estimator assumes that each subject has its specific characteristics due to inherent individual characteristic effects in the error term, thereby allowing differences to be intercepted between subjects. The random effects estimator assumes that the individual characteristic effect changes stochastically, and the differences in subjects are not fixed in time and are independent between subjects. To choose the right estimator, we run both models for each group of cities based on the Hausman specification test 83 . The null hypothesis is that random effects model yields consistent and efficient estimates 84 : \({H}_{0}{:}\,E\left({\varepsilon }_{i}|{X}_{it}\right)=0\) . If the null hypothesis is rejected, the fixed effects model will be selected for further inferences. Once the better estimator was determined for each model, one optimal panel data model was fit to each city group of one division type. In total, six, four, and eight runs were conducted for socioeconomic, spatial autocorrelation, and population division separately and three, two, and four panel data models were finally selected.

Spatial patterns of PM 2.5 level changes

During the period from 2000 to 2014, the annual mean PM 2.5 concentration of all cities increases from 27.78 to 42.34 µg/m 3 , both of which exceed the World Health Organization recommended annual mean standard (10 µg/m 3 ). It is worth noting that the PM 2.5 level in the year 2014 also exceeds China’s air quality Class 2 standard (35 µg/m 3 ) that applies to non-national park places, including urban and industrial areas. The standard deviation of annual mean PM 2.5 values for all cities increases from 12.34 to 16.71 µg/m 3 , which shows a higher variability of inter-urban PM 2.5 pollution after a decadal period. The least and most heavily polluted cities in China are Delingha, Qinghai (3.01 µg/m 3 ) and Jizhou, Hubei (64.15 µg/m 3 ) in 2000 and Hami, Xinjiang (6.86 µg/m 3 ) and Baoding, Hubei (86.72 µg/m 3 ) in 2014.

Spatially, the changes in PM 2.5 levels exhibit heterogeneous patterns across cities (Fig.  3 b). According to the socioeconomic level division (Fig.  3 a), the Eastern, Central, and Western region experienced a 38.6, 35.3, and 25.5 µg/m 3 increase in annual PM 2.5 mean , separately, and the difference among regions is significant according to the analysis of variance (ANOVA) results (Fig.  4 a). When stratified by spatial autocorrelation relationship (Fig.  3 c), the differences in PM 2.5 changes among the spatial clusters are even more dramatic. The average PM 2.5 increase in cities belonging to the high-high cluster is approximately 25 µg/m 3 , as compared to 5 µg/m 3 in the low-low clusters (Fig.  4 b). Finally, cities at four different population levels have significant differences in the changes of PM 2.5 concentration (Fig.  3 d), except for the mid-sized cities and large city agglomeration (Fig.  4 c).

( a ) Division of cities in China by socioeconomic development level and the locations of provincial capitals; ( b ) Changes in annual mean PM 2.5 concentrations between the year 2000 and 2014; ( c ) LISA cluster maps for PM 2.5 changes at the city level; High-high indicates a statistically significant cluster of high PM 2.5 level changes over the study period. Low-low indicates a cluster of low PM 2.5 inter-annual variation; No high-low cluster is reported; Low–high represents cities with high PM 2.5 inter-annual variation surrounded by cities with low variation; ( d ) Population level by cities in the year 2014. Maps were produced by ArcGIS 10.7.1 85 .

Boxplots of PM 2.5 concentration changes between 2000 and 2014 for city groups that are formed according to ( a ) socioeconomic development level division, ( b ) LISA clusters, and ( c ) population level. Asterisk marks represent the p value of ANOVA significant test between the corresponding pair of groups. Note ns not significant; * p value < 0.05; ** p value < 0.01; *** p value < 0.001; H–H high-high cluster, L–H low–high cluster, L–L denotes low–low cluster.

The effects of urban forms on PM 2.5 changes

The Hausman specification test for fixed versus random effects yields a p value less than 0.05, suggesting that the fixed effects model has better performance. We fit one panel data model to each city group and built nine models in total. All models are statistically significant at the p  < 0.05 level and have moderate to high predictive power with the R 2 values ranging from 0.63 to 0.95, which implies that 63–95% of the variation in the PM 2.5 concentration changes can be explained by the explanatory variables (Table 2 ).

The urban form—PM 2.5 relationships differ distinctly in Eastern, Central, and Western China. All models reach high R 2 values. Model for Eastern China (refer to hereafter as Eastern model) achieves the highest R 2 (0.90), and the model for the Western China (refer to hereafter as Western model) reaches the lowest R 2 (0.83). The shape metrics FRAC and CONTIG are correlated with PM 2.5 changes in the Eastern model, whereas the area metrics AREA demonstrates a positive effect in the Western model. In contrast to the significant associations between shape, area metrics and PM 2.5 level changes in both Eastern and Western models, no such association was detected in the Central model. Nonetheless, two aggregation metrics, LSI and AI, play positive roles in determining the PM 2.5 trends in the Central model.

For models built upon the LISA clusters, the H–H model (R 2  = 0.95) reaches a higher fitting degree than the L–L model (R 2  = 0.63). The estimated coefficients vary substantially. In the H–H model, the coefficient of CONTIG is positive, which indicates that an increase in CONTIG would increase PM 2.5 pollution. In contrast, no shape metrics but one area metrics AREA is significant in the L–L model.

The results of the regression models built for cities at different population levels exhibit a distinct pattern. No urban form metrics was identified to have a significant relationship with the PM 2.5 level changes in groups of very small and mid-sized cities. For small size cities, the aggregation metrics COHESION was positively associated whereas AI was negatively related. For mid-sized cities and large agglomerations, CONTIG is the only significant variable that is positively related to PM 2.5 level changes.

Urban form is an effective measure of long-term PM 2.5 trends

All panel data models are statistically significant regardless of the data group they are built on, suggesting that the associations between urban form and ambient PM 2.5 level changes are discernible at all city levels. Importantly, these relationships are found to hold when controlling for population size and gross domestic product, implying that the urban landscape patterns have effects on long-term PM 2.5 trends that are independent of regional economic performance. These findings echo with the local, regional, and global evidence of urban form effect on various air pollution types 5 , 14 , 21 , 22 , 24 , 39 , 78 .

Although all models demonstrate moderate to high predictive power, the way how different urban form metrics respond to the dependent variable varies. Of all the metrics tested, shape metrics, especially CONTIG has the strongest effect on PM 2.5 trends in cities belonging to the high-high cluster, Eastern, and large urban agglomerations. All those regions have a strong economy and higher population density 86 . In the group of cities that are moderately developed, such as the Central region, as well as small- and mid-sized cities, aggregation metrics play a dominant negative role in PM 2.5 level changes. In contrast, in the least developed cities belonging to the low-low cluster regions and Western China, the metrics describing size and number of urban patches are the strongest predictors. AREA and NP are positively related whereas TA is negatively associated.

The impacts of urban form metrics on air quality vary by urbanization degree

Based on the above observations, how urban form affects within-city PM 2.5 level changes may differ over the urbanization stages. We conceptually summarized the pattern in Fig.  5 : area metrics have the most substantial influence on air pollution changes at the early urban development stage, and aggregation metrics emerge at the transition stage, whereas shape metrics affect the air quality trends at the terminal stage. The relationship between urban form and air pollution has rarely been explored with such a wide range of city selections. Most prior studies were focused on large urban agglomeration areas, and thus their conclusions are not representative towards small cities at the early or transition stage of urbanization.

The most influential metric of urban form in affecting PM 2.5 level changes at different urbanization stages.

Not surprisingly, the area metrics, which describe spatial grain of the landscape, exert a significant effect on PM 2.5 level changes in small-sized cities. This could be explained by the unusual urbanization speed of small-sized cities in the Chinese context. Their thriving mostly benefited from the urbanization policy in the 1980s, which emphasized industrialization of rural, small- and mid-sized cities 87 . With the large rural-to-urban migration and growing public interest in investing real estate market, a side effect is that the massive housing construction that sometimes exceeds market demand. Residential activities decline in newly built areas of smaller cities in China, leading to what are known as ghost cities 88 . Although ghost cities do not exist for all cities, high rate of unoccupied dwellings is commonly seen in cities under the prefectural level. This partly explained the negative impacts of TA on PM 2.5 level changes, as an expanded while unoccupied or non-industrialized urban zones may lower the average PM 2.5 concentration within the city boundary, but it doesn’t necessarily mean that the air quality got improved in the city cores.

Aggregation metrics at the landscape scale is often referred to as landscape texture that quantifies the tendency of patch types to be spatially aggregated; i.e., broadly speaking, aggregated or “contagious” distributions. This group of metrics is most effective in capturing the PM 2.5 trends in mid-sized cities (population range 25–50 k) and Central China, where the urbanization process is still undergoing. The three significant variables that reflect the spatial property of dispersion, separately landscape shape index, patch cohesion index, and aggregation index, consistently indicate that more aggregated landscape results in a higher degree of PM 2.5 level changes. Theoretically, the more compact urban form typically leads to less auto dependence and heavier reliance on the usage of public transit and walking, which contributes to air pollution mitigation 89 . This phenomenon has also been observed in China, as the vehicle-use intensity (kilometers traveled per vehicle per year, VKT) has been declining over recent years 90 . However, VKT only represents the travel intensity of one car and does not reflect the total distance traveled that cumulatively contribute to the local pollution. It should be noted that the private light-duty vehicle ownership in China has increased exponentially and is forecast to reach 23–42 million by 2050, with the share of new-growth purchases representing 16–28% 90 . In this case, considering the increased total distance traveled, the less dispersed urban form can exert negative effects on air quality by concentrating vehicle pollution emissions in a limited space.

Finally, urban contiguity, observed as the most effective shape metric in indicating PM 2.5 level changes, provides an assessment of spatial connectedness across all urban patches. Urban contiguity is found to have a positive effect on the long-term PM 2.5 pollution changes in large cities. Urban contiguity reflects to which degree the urban landscape is fragmented. Large contiguous patches result in large CONTIG_MN values. Among the 626 cities, only 11% of cities experience negative changes in urban contiguity. For example, Qingyang, Gansu is one of the cities-featuring leapfrogs and scattered development separated by vacant land that may later be filled in as the development continues (Fig.  6 ). Most Chinese cities experienced increased urban contiguity, with less fragmented and compacted landscape. A typical example is Shenzhou, Hebei, where CONTIG_MN rose from 0.27 to 0.45 within the 14 years. Although the 13 counties in Shenzhou are very far scattered from each other, each county is growing intensively internally rather than sprawling further outside. And its urban layout is thus more compact (Fig.  6 ). The positive association revealed in this study contradicts a global study indicating that cities with highly contiguous built-up areas have lower NO 2 pollution 22 . We noticed that the principal emission sources of NO 2 differ from that of PM 2.5. NO 2 is primarily emitted with the combustion of fossil fuels (e.g., industrial processes and power generation) 6 , whereas road traffic attributes more to PM 2.5 emissions. Highly connected urban form is likely to cause traffic congestion and trap pollution inside the street canyon, which accumulates higher PM 2.5 concentration. Computer simulation results also indicate that more compact cities improve urban air quality but are under the premise that mixed land use should be presented 18 . With more connected impervious surfaces, it is merely impossible to expect increasing urban green spaces. If compact urban development does not contribute to a rising proportion of green areas, then such a development does not help mitigating air pollution 41 .

Six cities illustrating negative to positive changes in CONTIG_MN and AREA_MN. Pixels in black show the urban areas in the year 2000 and pixels in red are the expanded urban areas from the year 2000 to 2014. Figure was produced by ArcGIS 10.7.1 85 .

Conclusions

This study explores the regional land-use patterns and air quality in a country with an extraordinarily heterogeneous urbanization pattern. Our study is the first of its kind in investigating such a wide range selection of cities ranging from small-sized ones to large metropolitan areas spanning a long time frame, to gain a comprehensive insight into the varying effects of urban form on air quality trends. And the primary insight yielded from this study is the validation of the hypothesis that the determinants of PM 2.5 level trends are not the same for cities at various developmental levels or in different geographic regions. Certain measures of urban form are robust predictors of air quality trends for a certain group of cities. Therefore, any planning strategy aimed at reducing air pollution should consider its current development status and based upon which, design its future plan. To this end, it is also important to emphasize the main shortcoming of this analysis, which is generally centered around the selection of control variables. This is largely constrained by the available information from the City Statistical Yearbook. It will be beneficial to further polish this study by including other important controlling factors, such as vehicle possession.

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Acknowledgements

Lu Liang received intramural research funding support from the UNT Office of Research and Innovation. Peng Gong is partially supported by the National Research Program of the Ministry of Science and Technology of the People’s Republic of China (2016YFA0600104), and donations from Delos Living LLC and the Cyrus Tang Foundation to Tsinghua University.

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Liang, L., Gong, P. Urban and air pollution: a multi-city study of long-term effects of urban landscape patterns on air quality trends. Sci Rep 10 , 18618 (2020). https://doi.org/10.1038/s41598-020-74524-9

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• Original research article
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• Published: 01 June 2018

Outdoor air pollution and respiratory health: a bibliometric analysis of publications in peer-reviewed journals (1900 – 2017)

• Waleed M. Sweileh 1 ,
• Samah W. Al-Jabi 2 ,
• Sa’ed H. Zyoud 2 &
• Ansam F. Sawalha 1  

Multidisciplinary Respiratory Medicine volume  13 , Article number:  15 ( 2018 ) Cite this article

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Outdoor air pollution is a major threat to global public health that needs responsible participation of researchers at all levels. Assessing research output is an important step in highlighting national and international contribution and collaboration in a certain field. Therefore, the aim of this study was to analyze globally-published literature in outdoor air pollution – related respiratory health.

Outdoor air pollution documents related to respiratory health were retrieved from Scopus database. The study period was up to 2017. Mapping of author keywords was carried out using VOSviewer 1.6.6.

Search query yielded 3635 documents with an h -index of 137. There was a dramatic increase in the number of publications in the last decade of the study period. The most frequently encountered author keywords were: air pollution (835 occurrences), asthma (502 occurrences), particulate matter (198 occurrences), and children (203 occurrences). The United States of America ranked first (1082; 29.8%) followed by the United Kingdom (279; 7.7%) and Italy (198; 5.4%). Annual research productivity stratified by income and population size indicated that China ranked first (22.2) followed by the USA (18.8). Analysis of regional distribution of publications indicated that the Mediterranean, African, and South-East Asia regions had the least contribution. Harvard University (92; 2.5%) was the most active institution/organization followed the US Environmental Protection Agency (89; 2.4%). International collaboration was restricted to three regions: Northern America, Europe, and Asia. The top ten preferred journals were in the field of environmental health and respiratory health. Environmental Health Perspective was the most preferred journal for publishing documents in outdoor pollution in relation to respiratory health.

Research on the impact of outdoor air pollution on respiratory health had accelerated lately and is receiving a lot of interest. Global research networks that include countries with high level of pollution and limited resources are highly needed to create public opinion in favor of minimizing outdoor air pollution and investing in green technologies.

Outdoor air pollution is defined as the presence of one or more substances in the atmospheric air at concentrations and duration above the natural limits [ 1 ]. Such substances include ozone [O3], airborne lead [Pb], carbon monoxide [CO], sulphur oxides [SOx] and nitrogen oxides [NOx] [ 2 ]. Recently, air pollution with particulate matters (PM), especially those with less than 2.5 μm, has been the focus of most outdoor air pollution studies due to its ability to penetrate the lung tissue and induce local and systemic effects [ 2 ].

Air pollution has been described as one of the “great killers of our age” and as “major threat to health” due to its tremendous and various health effects on humans of all ages and in both genders [ 3 , 4 ]. In 2014, the World Health Organization (WHO) estimated that 92% of the world population was living in places with less than optimum outdoor air quality. Furthermore, WHO reported that in 2012, outdoor air pollution caused around 3 million deaths worldwide and 6.5 million deaths (11.6% of all global deaths) were associated with indoor and outdoor air pollution together [ 5 ].

Air pollution was linked to cancer, respiratory diseases, negative pregnancy outcomes, infertility, cardiovascular diseases, stroke, cognitive decline, and other adverse medical conditions [ 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 ]. Nearly 90% of air-pollution-related deaths occur in low- and middle-income countries, with nearly 2 out of 3 occurring in South-East Asia and Western Pacific regions. The problem of outdoor pollution is not a new one, but the rapid urbanization, particularly in Asia, made the problem of air pollution more visible and its health burden more tangible [ 14 , 15 , 16 , 17 ].

Bibliometric analysis is the application of statistical methods on published literature to analyze publication trends with time and to shed light on influential researchers, countries, and institutions in the field. In the past decade, at least seven bibliometric studies on air pollution were published [ 18 , 19 , 20 , 21 , 22 , 23 , 24 ]. However, none of the published bibliometric studies have shed light on the air pollution - related respiratory health. Therefore, in the current study, we aim to analyze literature pertaining to outdoor air pollution and respiratory health. The size of the literature and research productivity in this field is a good indicator of national and international efforts to improve air quality and to decrease the health and economic burden of air pollution. Furthermore, the quality of the air we breathe is the responsibility of everyone including researchers and academics. This study comes in line with perceived personal responsibility toward better air quality.

Search strategy

This study aimed to analyze the documents about outdoor air pollution – related respiratory health. Scopus database was used to retrieve relevant documents because of its advantages over other databases [ 25 , 26 , 27 , 28 ]. The search strategy developed for this study consisted of nine steps (Additional file  1 ). The first six steps utilized various keywords and search queries to retrieve the maximum number of documents. Keywords included in search queries were those found in recent relevant systematic reviews [ 6 , 12 , 13 , 29 , 30 ]. The combined result of search queries underwent a filtration process by adding exclusion and limitation components (steps 7 – 9).

False positive results were minimized by using title search. Therefore, all retrieved documents have the keywords of interest. Despite that, false positive results need to be searched by reviewing the retrieved documents. The review process was carried out on a sample of 200 documents chosen based on the number of citation. The review process was carried out by the authors (W.S and A.J) and approved by a third author (A.S). Keywords of the irrelevant documents (false positive results) were used in the exclusion step. A complete list of irrelevant keywords is written in Additional file 1 . The exclusion of false positive results is not enough to confirm the validity of the search strategy. Therefore, the authors compared two different methods of data collection. In the first one, we collected data regarding research output for each of the most active authors as obtained through the search strategy, whilst in the second one, the research output of each of the most active authors was extracted and reviewed by exploring the author profile as presented by Scopus. The extent of agreement between the two methods is measured by interclass correlation coefficient using SPSS [ 31 , 32 , 33 , 34 , 35 ]. An excellent agreement between the two methods with an interclass correlation above 95% and a p less than 5% is indicative of high validity of the search strategy. In the current study, the interclass correlation was 0.98.7% and p was 0.001.

The retrieved data were also sorted based on the number of different country affiliations per article to calculate international collaboration. Documents with authors from different countries represent international or inter-country collaboration while documents in which all authors have one country affiliation represent intra-country collaboration. It should be emphasized that Scopus has the function which can separate documents with intra or inter – country collaboration. Therefore, the calculation of international collaboration was extracted from data provided by Scopus.

Bibliometric analysis versus systematic reviews

It should be emphasized that the bibliometric analysis is not the same as systematic reviews. In contrast to systematic reviews, bibliometric analysis focuses on quantitative and qualitative aspects of all documents retrieved from one electronic large database. In bibliometric analysis, the investigated research question is the volume of research published, how this volume of literature evolved with time, what major topics were of high interest, and the scientific impact of literature in a particular subject. However, in systematic reviews, a complete and exhaustive summary of current literature obtained from several electronic databases and relevant to a research question is provided.

In bibliometric analysis, only one large and well – known database, such as Scopus, is used. Therefore, the retrieved documents will not include any duplicates. On the other hand, duplicate documents might appear in systematic reviews because several databases are used to retrieve the required documents.

Data analysis and visualization

In this study, Hirsch-index ( h -index) was used as a measure of impact of publications [ 36 ]. Graphs were created using Statistical Package for Social Sciences (SPSS). Hirsch - index is defined as the number of articles (n) that have received at least n citations [ 36 ]. VOSviewer software was used to create visualization maps while ArcMap 10.1 was used to create geographical distribution of the retrieved documents [ 37 , 38 , 39 ]. For VOSviewer mapping of most frequent author keywords, a minimum occurrence of 10 was used as a cut-off point for inclusion of the keyword in mapping analysis. Analysis also included distribution of publications based on World Health Organization (WHO) regions.

Types and growth of publications

The search strategy yielded 3635 documents. The earliest document in this field was published in 1943 in American Journal of Epidemiology [ 40 ]. The analysis of the types of documents showed that research articles (2935, 80.7%) were the most common type followed by review articles (359; 9.9%). The remaining documents (341; 9.4%) were conference papers, letters, editorials, short surveys, and notes. English (2923, 80.4%) was the primary language of documents followed by French (156; 4.3%) and German (124; 3.4%). The subject areas of the documents were medicine (2772; 76.3%) followed by environmental science (1038; 28.6%) and biochemistry/ genetics/ molecular biology (317; 8.7%) with the possibility of overlap among different subject areas. The growth of publications showed a dramatic increase in the past decade. Figure  1 shows the annual growth of publications. There was a 72% increase in number of publications in 2017 compared to that in 2008.

Annual growth of publications in Outdoor air pollution and respiratory health (1900 – 2017)

Author keywords

Analysis of author keywords showed that the most frequently encountered author keywords were: air pollution (835 occurrences), asthma (502 occurrences), particulate matter (198 occurrences), and children (203 occurrences) (Fig.  2a ). Further mapping of types of pollutants most commonly encountered in author keywords showed that particulate matter (198 occurrences), ozone (192 occurrences), nitrogen oxide (95 occurrences), PM10 (75 occurrences), PM2.5 (57 occurrences), and Sulfur dioxide (54 occurrences), were the most frequently encountered author keywords (Fig. 2b ).

Most frequent author keywords encountered in the retrieved documents ( a ) and most frequently encountered types of outdoor air pollutants encountered in the retrieved documents ( b )

Active journals

Table  1 shows the top ten journals that were involved in publishing the retrieved documents. Environmental Health Perspective was the most active journal (153; 4.2%) followed by Environmental Research (112; 3.1%) and American Journal of Respiratory and Critical Care Medicine (100; 2.8%). The top ten active journals included four in the field of environmental health, four in the field of respiratory health, one in allergy/immunology, and the last one in toxicology field.

Authorship analysis

The number of different author names who participated in publishing documents was 11,014; giving an average of 3.0 authors per document. Table  2 lists the top ten active authors with their affiliations. The top active authors were mainly from Western and Northern Europe, particularly from the Netherlands, Italy, and the United Kingdom [ 21 ]. Prof. Brunekreef, B. from the Netherlands was the most active researcher in this field with 77 (2.2%) documents. Authors with a minimum productivity of 20 publications were also visualized using network visualization map that showed research networking among active authors (Fig.  3 ). The map showed that active authors with minimum productivity of 20 publications existed in four clusters. The largest cluster consisted of eight authors (dark red color). The second cluster consisted of seven authors (green). The third cluster consisted of six authors (blue). The fourth cluster consisted of four researchers (dark yellow). Authors with minimum productivity of 20 publications who are not shown in the map are usually those who did not exist within a research network that has prominent productivity.

Network visualization map of authors with minimum productivity of 20 publications in the studied field and exist within a collaborative research group

Active countries

Researchers from 92 different countries contributed to the retrieved documents. Table  3 lists the top ten countries actively involved in air pollution – related respiratory health. Researchers from the USA participated in publishing 1082 (29.8%) documents. The top 10 list included countries from Northern America, Western Europe, and Asia. Researchers from these top ten countries participated in publishing 2630 (72.3%) documents. Figure  4 shows worldwide geographical distribution of retrieved documents. Regional distribution of retrieved documents indicated that the regions of Americas, Europe, and Western pacific had the highest percentage of contribution while Mediterranean, Africa, and South-East Asia regions had the least contribution (Fig.  5 ).

Geographical distribution of published research in outdoor air pollution and respiratory health (1900 – 2017)

Geographical distribution of published research in outdoor air pollution and respiratory health (1900 – 2017) based on WHO world region. WP: Western Pacific; EM: Eastern Mediterranean; E: Europe; SEA: South Eastern Asia; AM: Americas; AF: Africa

International collaboration

International collaboration in air pollution – related respiratory health showed that there were three clusters. There was relatively adequate collaboration among countries within each cluster and there was adequate collaboration between countries in the two different clusters (Fig.  6 ). The first cluster consisted of 9 European countries shown in green color while the second cluster consisted of 9 countries in different regions in the world particularly those in Northern and Southern America, South East Asia, and Western Pacific regions. The third cluster consisted of one item, India with research connections with countries in both cluster number 1 and 2. International collaboration among countries in the Mediterranean region, Africa, or Eastern Europe and those in Northern America, Europe, or Asia did not show up in the map.

Network visualization map of research collaboration in outdoor air pollution and respiratory health (1900 – 2017)

Table 3 shows the extent and the percentage of intra and inter (international) country collaboration for the top active countries. In terms of quantity, the USA had the largest number of documents (276; 25.5%) with international authors. However, this quantity represents only 25.5% of total research productivity from the USA which means that approximately 75% of USA research production in this field was produced by authors from the USA without collaboration with international researchers. Japan had the least percentage (22.2%) of international collaboration while Sweden had the largest percentage (58.0%) of documents with international collaboration.

Active institutions

Harvard University ranked first in research output (92; 2.5%) and in the impact of publications ( h -index = 44). The US Environmental Protection Agency (EPA) ranked second in research output (89; 2.4%) and in the impact of publications ( h -index = 36). Table  4 shows the top ten active institutions/organizations. The list included seven academic institutions and three research centers. Six of the top active institutions were American institutions, three were European, and one was Canadian.

Citation analysis

The total number of citations received by documents was 101,113, with an average of 27.8 citations per document. Range of citations was [0 – 4294]. The h -index of the retrieved documents was 137. Table  5 shows the top ten highly cited articles. The article that received the highest number of citations (4294) was published in 2002 in Journal of American Medical Association (JAMA) and discussed the relation between lung cancer, cardiopulmonary mortality and air pollution [ 40 ].

Growth of publications

In this study, we analyzed global research output in outdoor air pollution – related respiratory health. The results showed a noticeable increase in the number of publications in the last decade of the study period. This indicates that the level of air pollution and its health consequences reached serious levels. In 2012, air pollution was responsible for 3 million deaths, representing 5.4% of the total global deaths. In the same year, about 25% were due to lung cancer deaths, 8% were due to chronic obstructive pulmonary disease (COPD) deaths, and about 17% of respiratory infection deaths were caused by outdoor air pollution [ 41 ]. A study indicated that the contribution of outdoor air pollution to global premature mortality could double by 2050 [ 42 ]. Another study concluded that outdoor air pollution contributes to the increase in global burden of COPD and that an increase of 10 μg/m (3) in PM10 produced significant increase in COPD deaths and exacerbations that can be substantially reduced by controlling air pollution [ 43 ]. A cohort Chinese study concluded that the risks of mortality and years of life lost were elevated corresponding to an increase in current ambient concentrations of the air pollutants [ 44 ].

The contribution of researchers from /ifferent scientific fields led to an acceleration in the growth of publications in this field. Scientists in the fields of the environment, respiratory health, public health, and even molecular biology/genetics contributed to the retrieved documents [ 45 , 46 , 47 , 48 , 49 ]. The fact that air pollution is a multidisciplinary field created a large number of readers from different scientific fields and thus leading to large number of citations, reflected in the relatively high h -index value of documents. For example, the h -index of literature in global carbapenem resistance was 102 and that for literature in resistant tuberculosis was 76 [ 50 , 51 ].

Active countries and institutions

Our results showed that China had the highest research productivity in terms of GDP per capita per year. In China, air pollution was previously estimated to contribute to 1.2 to 2 million deaths annually [ 52 ]. In its list of the world’s deadliest countries for air pollution, the WHO ranked China first followed by India, Russian Federation, Indonesia, Pakistan, Ukraine, Nigeria, Egypt, USA, and Bangladesh [ 53 ]. Out of the top 10 countries that have high total annual number of deaths from PM2.5 and PM10, only China and USA were among the top ten active countries in research output. The deadliest effects of air pollution in China led to the adoption of the Ambient Air Quality Standard in China in 2012 [ 54 ]. This system started a national Air Reporting System that now includes 945 sites in 190 cities.

The presence of active institutions and many high impact journals in the field of environmental health and respiratory medicine issued from the USA contributed to the leadership of USA in this field. Research output in any field is a function of money allocated to research as well as public health agendas of the country. The leadership of the USA was seen in several other scientific subjects [ 55 , 56 , 57 , 58 ]. The fact that English was the primary language of literature published in journals indexed in Scopus might have created some sort of bias toward English-speaking countries.

Outdoor air pollution is a global health concern and international collaboration in this field is necessary. In our study, the extent of international collaboration in research was relatively high, particularly within European countries and between USA and Asian countries. The WHO Collaborating Centre for Air Quality Management and Air Pollution Control (WHO CC) is working with member states in Europe and Asia to encourage collaboration in air quality programs through interaction and networking [ 59 ].

Highly cited documents

The top cited documents in the field was about the relationship between outdoor air pollution and lung cancer; and received a large number of citations suggestive of great importance. The International Agency for Research on Cancer [ 60 ], which is part of the WHO, has classified outdoor air pollution, as a whole, as a cancer-causing agent (carcinogen ) [ 60 ]. The International Agency for Research on Cancer (IARC) concluded that outdoor air pollution causes lung cancer and is associated with increased risk for bladder cancer . Urgent action to minimize level of outdoor air pollution and exposure of population to such carcinogenic pollutants is necessary, particularly in cities with high levels of outdoor air pollution [ 61 , 62 ].

Strength and limitations

It is the first to assess research activity in the field of outdoor air pollution – related respiratory health. Our study documented the accelerated increase in publications and the role of international collaboration. However, our study has a number of limitations. The Scopus database is a comprehensive and large database that includes different disciplines, but some peer – reviewed journals are not indexed in Scopus. This is particularly true for journals published from India, China, Indonesia, and other Asian and African countries where outdoor air pollution is a real public health problem. Therefore, documents published in un-indexed journals were not retrieved. Secondly, the results presented in this study reflect the search strategy implemented which is comprehensive in the subject but the presence of false positive and false negative results cannot be ruled out. This is true in all bibliometric studies [ 63 , 64 , 65 , 66 , 67 ]. Thirdly, when listing active authors and institutions, the authors depended on the outcome obtained from the Scopus. However, some authors might have more than one Scopus profile or might have their name written in different articles in different ways which will affect their research output and therefore their rank as well. Same applies to active institutions where the name of the institution might be written in different articles in different ways which will affect their research output and rank as well. Furthermore, the authors used the keyword “environment” in the search strategy in a strict way to avoid false positive results since not all environmental pollution could fit the scope of the current study which focused on outdoor air pollution and its impact on respiratory health. In this regard, the authors also avoided the use of the keyword “climate” in the search strategy to keep the research question focused on outdoor air pollution, particularly those produced by industry. Finally, it should be emphasized that the list of highly cited articles does not mean that these articles are the only influential ones in the field. The citation process is dynamic and sometimes high citation reflects self-citation rather than impact. There were many influential and highly cited articles in the field that were not listed in the highly cited article [ 68 , 69 , 70 , 71 , 72 , 73 , 74 , 75 , 76 , 77 , 78 , 79 ].

Conclusions

Growth of publications in outdoor air pollution – related respirator health is rapidly increasing. However, limited research output and international collaboration were seen in world regions such as the Middle East, Africa, and South-East Asia. International multidisciplinary research network, involving countries with high levels of air pollution and limited resources, are needed. Research in atmospheric pollution should also be directed toward prevention of air pollution problems by investing more in green technology.

The results presented in this study are indicative of how research activity is interacting with the urgent acceleration of the air pollution crisis at the global level. Furthermore, the research activity is indicative of the response rate adopted by certain countries to face this global problem in a responsible way. Pressure groups can use the research activity to enforce certain environmental and industrial agendas on politicians and political campaigns. Countries with high levels of outdoor air pollution, and therefore, poor air quality, need to get engaged in research pertaining to this field to provide health policymakers with baseline data for future action. Establishing research center for monitoring national air quality and level of air pollution is a step forward that needs to be adopted by all countries. Such centers could include scientists from different disciplines who can collaborate to convert research findings into national agendas and policies. At the national levels, different world countries need to adopt strict guidelines for air quality. Collaboration between industry and health authorities is needed to implement measures that could significantly reduce the levels of particulate matter. The outdoor air pollution is a global public health and therefore research networking between developed countries and developing countries with high levels of air pollution should be prioritized. The Chinese model in controlling air pollution and minimizing its health consequences could be of a global benefit. Finally, since the respiratory effects of air pollution are affecting children, there is a need to educate and increase the awareness of parents regarding this issue.

Abbreviations

International Agency for Research on Cancer

Institutional Review Board

World Health Organization

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Sweileh, W.M., Al-Jabi, S.W., Zyoud, S.H. et al. Outdoor air pollution and respiratory health: a bibliometric analysis of publications in peer-reviewed journals (1900 – 2017). Multidiscip Respir Med 13 , 15 (2018). https://doi.org/10.1186/s40248-018-0128-5

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Acknowledgements

This work was supported by the scholarship of Excellence from the National Center for Scientific and Technical Research (CNRST) of Morocco and Université du Littoral Côte d’Opale of Dunkerque, France. We would like to thank Atmo Hauts-de-France for providing us the measurements used in the study case.

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Karroum, K., Lin, Y., Chiang, YY. et al. A Review of Air Quality Modeling. MAPAN 35 , 287–300 (2020). https://doi.org/10.1007/s12647-020-00371-8

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A Literature Review of the Effects of Energy on Pollution and Health

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This paper reviews recent economic studies that estimate the impacts of energy accidents and energy-related policies and regulations on pollution and health. Using difference-in-differences and regression discontinuity designs, most papers show that energy accidents and consumption significantly increased pollution and had adverse health effects. However, the enforcement of clean energy policies and strict regulations have improved air quality and mitigated the negative effects on health. Hence, future research should focus more on the health effects of clean energy in developing countries.

I. Introduction

Many people live in places where the average air quality is above the World Health Organization’s suggested limits for pollutants. Pollution is currently the most severe environmental problem and public health concern in the world, especially in developing countries. Studies have mostly focused on the causal effect of pollution on health, and numerous economic studies have documented that pollution is severely harmful to human health, in both developed and developing countries.

With rapid industrialization, energy consumption has increased dramatically. As energy raises our quality of life to higher levels, it also puts our health at risk. There is now a growing literature on the impacts of energy accidents and consumption on pollution and health. It finds that pollution is the most relevant channel related to the health impacts of energy. Recent research suggests that the health of pregnant women, children, and infants is at greater risk of being adversely affected by pollution (e.g., Currie & Neidell , 2005; Janke , 2014 ). Many studies suggest that infants are sensitive to the pollution caused by oil spills and coal smoke. In addition, studies are increasingly suggesting that clean energy policies and environmental regulations can abate the negative health effects of energy-related pollution.

This paper reviews the recent literature that studies the effects of energy on pollution and health. The core of this causal evaluation addresses the endogeneity problem. Using exogenous energy accidents and policy experiments and regulations as exogenous events to test causality, the literature is mainly based on difference-in-differences and regression discontinuity designs.

On the one hand, given the population explosion and rapid urbanization, energy consumption and the volume of long-distance energy transnational transportation has increased rapidly, and energy accidents, such as the oil spill in the Gulf of Mexico in 2010 and the nuclear leak of Fukushima Daiichi in Japan, are occurring frequently. Therefore, research has documented the impacts of energy accidents on pollution and human health. In response to public concerns over the increasing pollution threat from energy consumption and accidents, governments can put forward various policies and regulations to mitigate the negative health effects. Therefore, this paper aims to review the recent literature and determine how energy affects pollution and health.

II. Impacts of energy accidents and consumption on pollution and health

Table 1 lists and categorizes selected economic studies on the impacts of energy accidents and consumption on pollution and health. Environmental disasters caused by energy accidents and general energy consumption can cause pollution, and the problem of human exposure to pollution is attracting greater attention. The literature on the health effects of oil pollution has also been growing rapidly. Many recent papers find oil to have a negative impact on pollution and health. Beland and Oloomi (2019) and Marcus (2021) quantify the health impact of petroleum pollution on infant health in the United States by using the 2010 oil spill in the Gulf of Mexico and the leakage of underground storage tanks as exogenous events. Exploiting a difference-in-differences design, Beland and Oloomi (2019) find that the oil spill of 2010 raised concentrations of PM 2.5 , NO 2 , SO 2 , and CO in the affected coastal counties and increased the incidence of low-birthweight and prematurely born infants. Marcus (2021) finds that exposure to a leaking underground storage tank during gestation increases the probability of both low birthweight and preterm birth by 7–8%. In addition, all the studies mentioned above find that prenatal exposure to pollution had a heterogeneous impact on mothers with different individual characteristics (age, race, marital status, etc.). The infants of black, Hispanic, less educated, unmarried, and younger mothers suffer from more pronounced adverse health outcomes (Beland & Oloomi , 2019) . Marcus (2021) also finds that the adoption of preventative technologies mitigated the entire effect of storage tank leak exposure on birthweight, and information increased avoidance and moving among highly educated mothers.

In addition to environmental disasters such as oil spills and leakage, general energy consumption has an impact on air quality and health. In particular, the decline in air quality caused by coal fires and straw burning leads directly to an increase in mortality. Beach and Hanlon (2018) provide the first estimate of the mortality effects of British industrial coal use in 1851–1860. The results indicate that local industrial pollution had a powerful impact on mortality. Raising local industrial coal use by one standard deviation above the mean increased infant mortality by roughly 6–8% and mortality among children under five by 8–15%.

Farmers often burn straw after a harvest, which is the main cause of seasonal air pollution in developing countries. Based on agricultural straw burning satellite data, He et al. (2020) use non-local straw burning as an instrumental variable for air pollution to estimate the impact of straw burning on air pollution and health. The results show that straw burning increased particulate matter pollution and caused people to die from cardiorespiratory diseases. Middle-aged individuals and the elderly in rural areas are more sensitive to such pollution. Furthermore, using a difference-in differences approach, the authors find that China’s recent straw recycling policy has effectively improved the country’s air quality.

III. Impacts of energy policies and regulations on pollution and health

Due to the negative impacts of energy accidents and consumption on pollution and health, more and more energy-related policies and regulations are established by governments to alleviate the negative effects of air quality on health. These policies and regulations provide exogenous shocks for identifying a causal relationship. A growing research literature has begun to focus on how energy policies and energy-related environmental regulations can affect pollution and health. As Table 2 shows, many papers examine the causal relationship between energy policies or regulations and pollution (health), using a difference-in-differences design and regression discontinuity analyses. Imelda (2020) investigates the health impacts of household access to cleaner fuel through a nationwide fuel-switching program. The results suggest that the program led to a significant decline in infant mortality and that fetal exposure to indoor air pollutants is an important channel. Fan et al. (2020) focus on China’s coal-fired winter heating systems, the country with the largest and most expensive energy welfare policies in the developing world. The authors estimate the contemporaneous impact of winter heating on air pollution and health by using regression discontinuity designs and find that such winter heating systems increased the weekly Air Quality Index by 36% and mortality by 14%. Fan et al. (2020) and Imelda (2020) both suggest that the health impact of air pollution can be mitigated by improving socioeconomic conditions.

Based on the fact that coal and gasoline fuels emit a huge number of harmful pollutants, many targeted environmental regulations have been enforced to reduce pollution and improve health welfare. Yang and Chou (2018) find that the shutdown of power plants located upwind of New Jersey reduced the likelihood of having a low-birthweight or preterm infant by 15% and 28%, respectively. Strict command-and-control energy regulation policies have obvious effects. Because of limited financial resources and staff, developing countries are constrained in terms of their regulatory capacity to enforce regulations. Evidence from better developed economies might not be readily generalizable to the developing world. Li et al. (2020) and Zhu and Wang (2021) examine the causal effects of China’s prefecture-level fuel standards and fuel content regulation on air pollution at ports. Their papers bridge the gap by documenting novel empirical evidence and measure the benefits of fuel standards in the world’s largest developing country. Li et al. (2020) find that the enforcement of high-quality gasoline standards significantly reduced average pollution by 12.9%, and improved air quality. Their conclusion demonstrates the importance of fuel quality. Zhu and Wang (2021) show that fuel content regulation at ports immediately reduced major types of pollutants by more than 15%.

IV. Conclusion

To sum up, the economic studies on the impact of energy on pollution and health are becoming richer. Methodologically, these studies typically use difference-in-differences and regression discontinuity designs to evaluate the causal effects of energy accidents, policies, and regulations on pollution and health. Several studies support negative effects on air quality and infant health, and all of these find that energy policies and regulations can reduce regional pollution and improve health welfare, especially in less developed countries and areas. Furthermore. resources and information should target pregnant women to help mitigate poor infant health. The findings can have important implications for countries besides China, including developing ones.

The papers covered in this review contribute to cost–benefit analysis by quantifying the pollution and health effects from energy accidents, policies, and regulations. The largest energy transition projects are being attempted in many developing countries, and further research is needed to fully understand the long-term health effects of clean energy in developing countries.

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A Literature Review on Prediction of Air Quality Index and Forecasting Ambient Air Pollutants using Machine Learning Algorithms

2020, Volume 5 - 2020, Issue 8 - August

Day by day the air pollution becomes serious concern in India as well as in overall world. Proper or accurate prediction or forecast of Air Quality or the concentration level of other Ambient air pollutants such as Sulfur Dioxide, Nitrogen Dioxide, Carbon Monoxide, Particulate Matter having diameter less than 10µ, Particulate Matter having diameter less than 2.5µ, Ozone, etc. is very important because impact of these factors on human health becomes severe. This literature review focuses on the various techniques used for prediction or modelling of Air Quality Index (AQI) and forecasting of future concentration levels of pollutants that may cause the air pollution so that governing bodies can take the actions to reduce the pollution.

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International Journal for Research in Applied Science & Engineering Technology (IJRASET)

IJRASET Publication

The proposed system depicts various strategies utilized for forecast of Air Quality Index (AQI) utilizing supervised machine learning procedures. The system examines machine learning algorithm for air quality index by computing algorithm accuracy which will bring about the best precision. Moreover, the system exhibits different machine learning accuracy figures from the given dataset with assessment order report which recognizes the perplexity lattice. The outcome shows the adequacy of machine learning suggested calculation method that can be contrasted and best exactness with accuracy, Recall and F1 Score. The air pollution database contains data for each state of India. Four supervised machine learning algorithms, decision tree, random forest tree, Naïve Bayes theorem and K-nearest neighbor are compared and evaluated. ORGANIZATION OF THE THESIS In this thesis, Introduction and architecture of our system, the objective and the problem statement will be discussed in Chapter 1. The Review of Literature will be discussed in Chapter 2. Chapter 3 will consist of Work Done on various modules of the project. The System Design is explained in the Chapter 4. The Results obtained by usingvarious algorithms are mentioned in the Chapter 5. The Chapter 6 will consist of Conclusion. At last, we have mentioned the Appendix and the References. I.

International Journal of Scientific Research in Computer Science, Engineering and Information Technology

International Journal of Scientific Research in Computer Science, Engineering and Information Technology IJSRCSEIT

Air pollution is the “world’s largest environmental health threat”[1], causing 7 million deaths[1] worldwide every year. Its major constituents are PM2.5, PM10 and the harmful green house gases S02, N02, C0 and other effluents from vehicles and factories affecting not only humans but also other living organisms both on land and sea. The only effective solution to this global issue is to implement machine learning algorithms to predict the AQI (Air Quality Index ) that can make the people aware of the condition of the air of a certain region such that certain actions could be issued by the government for the improvement of the air quality in the future. The prime objective behind this project is to predict the AQI based on the concentration of PM2.5, PM10,S02, N02, C0 as well as weather conditions like temperature, pressure and humidity[2].Hence the data set is combined from various web sources like cpcb.nic.in and uci repository in order to bring accuracy in the prediction and to justify whether the Quality of air is suitable or not. This prediction will be brought about with the help of some supervised machine learning algorithms and the observation and the result will state which algorithm is giving better accuracy in prediction of AQI and which one is giving less error.

IRJET Journal

The air quality monitoring system collects data of pollutants from different location to maintain optimum air quality. In the current situation, it is the critical concern,. The introduction of hazardous gases into the atmosphere from industrial sources, vehicle emissions, etc. pollutes the air. Today, the amount of air pollution in large cities has surpassed the government-set air quality index value and reached dangerous levels. It has a significant effect on a human health. The prediction of air pollution can be done by the Machine Learning (ML) algorithms. Machine Learning (ML) combines statistics and computer science to maximize the prediction power. ML is used in order to calculate the Air Quality Index. Various sensors and an Arduino Uno microcontroller are utilized to collect the dataset. Then by using K-Nearest Neighbor (KNN) algorithm, the air quality is predicted.

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