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Understanding the complex impact of air pollution is crucial to assessing exposure risk and defining public health policies in China. However, the evidence and hence knowledge of how urban activity responds to air pollution are limited. In this paper, we propose to use geotagged check-in records on Weibo, a Tweeter-like platform, to systematically investigate the effect of air pollution on urban activity. Based on panel models, we found clear evidence that such effect exists and varies between pollutants, visitors and residents, and different activity types. Typically, SO 2 has the largest impact, followed by PM 2.5 , NO 2 , and PM 10 ; local citizens' activities are more susceptible than visitors; leisure-related activity has a sensitivity at least twofold higher than work-related activities. Additionally, we tested hypotheses about the heterogeneous effect. We confirmed the role of Income and air quality, showing that people who live in richer and more polluted cities are more likely to experience the effects of air pollution. Specifically, people who live in a more polluted city with 100 unit increments in AvgAQI show on average the same sensitivity as those who live in a less polluted city and earn about 20.3 thousand yuan more in average Income. This reveals new insights about environmental injustice in China. By presenting a portrait of the spatial heterogeneity, we argued that environmental injustice in terms of air pollution is not just about the difference in exposure risk measured based on population distribution, rather the measurement should also consider the disparity derived from urban activity. Secondly, new injustice may arise in underdeveloped areas where manufacture industry is transferred to but people barely take avoidance behavior. Finally, the map also reveals the general neglect of the detrimental effect of light air pollution, which we speculate is partly due to China's comparatively low standard in governmental regulations. We believe our finding contributes significantly to exposure risk assessment and environmental justice debates. Hence it highlights the necessity and urgency of public healthy polices that spread the health consequence of air pollution, especially in the underdeveloped region. • Assessing exposure risk in China. • How urban activity responds to air pollution. • Using geo-tagged check-ins on Weibo, a Twitter-like platform, to investigate effects of air pollution on urban activity. [ABSTRACT FROM AUTHOR]

Poor air quality is the world's single largest environmental health risk, and air quality monitoring is crucial for developing informed air quality policies. Efforts to monitor air pollution in different countries are uneven, largely due to the high capital costs of reference air quality monitors (AQMs), especially for airborne particulate matter (PM). In sub-Saharan Africa, for example, few cities operate AQM systems. It is thus important to examine the potential of alternative monitoring approaches. Although PM measurements can be obtained from low-cost optical particle counters (OPCs), data quality can be an issue. This paper develops a new method using raw aerosol size distributions from multiple, surface-based low-cost OPCs to constrain the Multiangle Imaging SpectroRadiometer (MISR) component-specific, column aerosol optical depth (AOD) data, which contain some particle-size-resolved information. The combination allows us to derive surface aerosol concentrations for particles as small as ∼0.1 µ m in diameter, which MISR detects but are below the OPC detection limit of ∼0.5 µ m. As such, we obtain better constraints on the near-surface particulate matter (PM) concentration, especially as the smaller particles tend to dominate urban pollution. We test our method using data from five low-cost OPCs deployed in the city of Nairobi, Kenya, from 1 May 2016 to 2 March 2017. As MISR passes over Nairobi only once in about 8 d, we use the size-resolved MISR AODs to scale the more frequent Moderate Resolution Imaging Spectrometer (MODIS)-derived AODs over our sites. The size distribution derived from MISR and MODIS agrees well with that from the OPCs in the size range where the data overlap (adjusted- R2∼0.80). We then calculate surface-PM concentration from the combined data. The situation for this first demonstration of the technique had significant limitations. We thus identify factors that will reduce the uncertainty in this approach for future experiments. Within these constraints, the approach has the potential to greatly expand the range of cities that can afford to monitor long-term air quality trends and help inform public policy. [ABSTRACT FROM AUTHOR]

Poor air quality is the world's single largest environmental health risk, and air quality monitoring is crucial for developing informed air quality policies. Efforts to monitor air pollution in different countries are uneven, largely due to the high capital costs of reference air quality monitors (AQMs), especially for airborne particulate matter (PM). In sub-Saharan Africa, for example, few cities operate AQM systems. It is thus important to examine the potential of alternative monitoring approaches. Although PM measurements can be obtained from low-cost optical particle counters (OPCs), data quality can be an issue. This paper develops a new method using raw aerosol size distributions from multiple, surface-based low-cost Optical Particle Counters (OPCs) to calibrate the Multi-angle Imaging SpectroRadiometer (MISR) component-specific, column aerosol optical depth (AOD) data, that contain some particle-size-resolved information. The combination allows us to derive surface aerosol concentrations for particles as small as ~ 0.1 µm in diameter that MISR detects but are below the OPC detection limit of ~ 0.5 µm. As such, we obtain better constraints on the near-surface particulate matter (PM) concentration, especially as the smaller particles tend to dominate urban pollution. We test our method using data from five low-cost OPCs deployed in the city of Nairobi, Kenya, from May 1 2016 to March 2 2017. As MISR passes over Nairobi only once in about eight days, we use the size-resolved MISR AODs to scale the more frequent Moderate Resolution Imaging Spectrometer (MODIS)-derived AODs over our sites. The size distribution derived from MISR and MODIS agrees well with that from the OPCs in the size range where the data overlap (adjusted-R² ~ 0.80). We then calculate surface-PM concentration from the combined data. The situation for this first demonstration of the technique had significant limitations. We thus identify factors that will reduce the uncertainty in this approach for future experiments. Within these constraints, the approach has the potential to greatly expand the range of cities that can afford to monitor long-term air quality trends and help inform public policy. [ABSTRACT FROM AUTHOR]

Air pollution is a major threat to public health. However, two issues have not been adequately addressed in most conventional Land Use Regression models for air pollution prediction: 1). A combination of urban forest involvement and urban form representation; 2). Scale sensitivity analysis of model variables. Here, we apply lacunarity to investigate the spatial sensitivity of predictors, incorporate 2-D and 3-D urban form to comprehensively characterize the urban environment, and examine the tree diversity impacts on air pollution distribution using unique NO 2 datasets collected through opportunistic mobile monitoring in the Bronx, New York, and Oakland, California. We find that lacunarity-optimized models could reduce the computation burden by extracting the upper limits of the spatial heterogeneity of predictors while keeping the model accuracy simultaneously. Furthermore, there are synthetic effects between the urban form and tree diversity on NO 2 distribution, and such effect directions could be non-monotonic. Finally, although the increase in tree diversity could facilitate the reduction of regional NO 2 concentration, it is essential to seek a balance between tree diversity and tree dominance to effectively improve air quality on the city scale. The findings are useful for environmental scientists striving for better air quality and urban planners caring for the well-being of cities. [Display omitted] • Examined the interaction between the urban forest, physical environment, and air quality with mobile measurements. • Characterized the urban environment through both 2D and 3D urban morphological measurements. • Quantified spatial heterogeneity of regression predictors using lacunarity analysis. • The synthetic effects of the urban form and tree diversity on NO 2 distribution could be non-monotonic. • Seek a balance between tree diversity and tree dominance is essential to effectively improve air quality on the city scale. [ABSTRACT FROM AUTHOR]