Air Pollution Variables Research Articles

Distribution of COVID-19 Cases According to Environmental Pollutants and Meteorological Factors in Districts of Maharashtra

Abstract Introduction: Environmental factors such as temperature, precipitation, and various other pollutants in the air like nitrogen dioxide (NO2), CO2, etc., have been studied for sustenance and transmission of Coronavirus disease-19 (COVID-19). Studies from India are lacking about the distribution of COVID-19 cases according to air pollutants and meteorological factors. Hence, the present study was conducted to study the relationship between COVID-19 and environmental factors with the objectives of Cluster mapping of the COVID-19 cases district-wise for the state of Maharashtra. Methodology: The air pollution variables NO2 column density (mol/m2), sulfur dioxide column density (mol/m2), Aerosol absorbing index (Ai), Carbon monoxide (CO) column density (mol/m2), and Ozone column density (mol/m2) were extracted from Sentinel-5P datasets available in Google Earth Engine (GEE) (https://code.earthengine.google.com/). The meteorological variables precipitation (m), temperature (t), and humidity (k) were extracted from the European Environmental Agency 5-Land reanalysis dataset in GEE, which provides data at a resolution of 1113.2 m. The preprocessing and retrieval of data were carried out in GEE. These values were correlated with daily cases, density, and other demographic factors. Results: The study identified a cluster of cases in few districts of Maharashtra. These districts were Pune, Ahmednagar, Mumbai, Nashik, and Nagpur. Population density and NO2 were important factors in most of the monthly data. Conclusion: The present study used data available on public platforms to assess the association between COVID-19 and environmental factors. The study found that various environmental factors such as NO2, CO, and Ai may be associated with increase cases of COVID-19 in the district. However, ecological bias may be kept in mind.

Modeling NO2 air pollution variation during and after COVID-19-regulation using principal component analysis of satellite imagery

By implementing Principal Component Analysis (PCA) of multitemporal satellite data, this paper presents modeling solutions for air pollutant variation in three scenarios related to COVID-19 lockdown: pre, during, and after lockdown. Tropospheric NO2 satellite data from Sentinel-5P was used. Two novel PCA-models were developed: Weighted Principal Component Analysis (WPCA) and Rescaled Principal Component Analysis (RPCA). Model results were tested for goodness-of-fit to empirical NO2 data. The models were used to predict actual near-surface NO2 concentrations. Model-predicted NO2 concentrations were validated with NO2 data acquired at ground monitoring stations. Besides, meteorological bias affecting NO2 was assessed. It was found that the weather component had substantial impact on NO2 built-ups, propitiating air pollutant decrease during lockdown and increase after. WPCA and RPCA models well fitted to observed NO2. Both models accurately estimated near-surface NO2 concentrations. Modeled NO2 variation results evidenced the prolongated effect of the total lockdown (up to half a year). Model-predicted NO2 concentrations were found to highly correlate with monitoring station NO2 data collected on the ground. It is concluded that PCA is reliable in identifying and predicting air pollution variation patterns. The implementation of PCA is recommended when analyzing other pollutant gases.

Evaluation of total ozone measurements from Geostationary Environmental Monitoring Spectrometer (GEMS)

Abstract. The continued interest in air pollution and stratospheric ozone variability has motivated the development of a Geostationary Environmental Monitoring Spectrometer (GEMS) for hourly ozone monitoring. This paper provides the atmospheric science community with the world's first assessment of GEMS total column ozone (TCO) retrieval performance and diurnal ozone variation. The algorithm used for GEMS is a more advanced version of its predecessor, the Total Ozone Mapping Spectrometer (TOMS) V8, that incorporates several improvements, including a new lookup table, a simple Lambertian-equivalent reflectivity model, and a spectral dependence correction. The GEMS algorithm also uses the optimal estimation method (OEM) to make error analysis more accessible and robust. The estimated retrieval errors range from 1.5 to 2 DU in September and 2 DU in December, with a constant degree of freedom of the signal (DFS) of 1 in September and a variable DFS of 1.25 to 1.4 in December throughout the day, depending on solar zenith angle (SZA). To assess the performance of the GEMS algorithm, the hourly GEMS total ozone was compared with ground-based measurements from Pandora instruments and other satellite platforms from TROPOMI (TROPOspheric Monitoring Instrument) and OMPS (Ozone Mapping and Profiler Suite Nadir Mapper). GEMS has a high correlation of 0.97 and small RMSE values compared to Pandora TCO at Busan and Seoul in South Korea. It is notable that despite exhibiting seasonal dependence in the mean bias of GEMS with Pandora, GEMS is capable of observing daily variations in ozone that are highly consistent with Pandora measurements, with a bias of approximately 1 %. The comparison of GEMS TCO data with TROPOMI and OMPS TCO data shows a high correlation of 0.99 and low RMSE compared to TROPOMI and OMPS TCO data, but the data have a negative bias of −2.38 % and −2.17 %, with standard deviations of 1.33 % and 1.57 %, respectively. Similar to OMPS, the influence of SO2 from volcanic eruptions is not properly removed in some regions, leading to GEMS overestimating TCO in those areas. The mean biases of GEMS TCO data with TROPOMI and OMPS TCO are within ±1 % at low latitudes but become negative at midlatitudes, with an increasingly negative dependence on latitude. Furthermore, this dependence becomes more prominent from summer to winter. The empirical correction applied to the GEMS irradiance data improves the dependence of the mean bias on season and latitude, but a consistent bias still remains, and a marginal positive trend was observed in December. Therefore, further investigation into correction methods is needed. The results are a meaningful scientific advance by providing the first validated, hourly UV ozone retrievals from a satellite in geostationary orbit. This experience can be used to advance research with future geostationary environmental satellite missions, including the incoming TEMPO (Tropospheric Emissions: Monitoring of Pollution) and Sentinel-4.

Ultrafine particulate matter exposure during second year of life, but not before, associated with increased risk of autism spectrum disorder in BKMR mixtures model of multiple air pollutants

Prenatal and early postnatal air pollution exposures have been shown to be associated with autism spectrum disorder (ASD) risk but results regarding specific air pollutants and exposure timing are mixed and no study has investigated the effects of combined exposure to multiple air pollutants using a mixtures approach. We aimed to evaluate prenatal and early life multipollutant mixtures for the drivers of associations of air pollution with ASD. This study examined 484 typically developing (TD) and 660 ASD children from the CHARGE case-control study. Daily air concentrations for NO2, O3, ultrafine (PM0.1), fine (PM0.1-2.5), and coarse (PM2.5-10) particles were predicted from chemical transport models with statistical bias adjustment based on ground-based monitors. Daily averages were calculated for each exposure period (pre-pregnancy, each trimester of pregnancy, first and second year of life) between 2000 and 2016. Air pollution variables were natural log-transformed and then standardized. Individual and joint effects of pollutant exposure with ASD, and potential interactions, were evaluated for each period using hierarchical Bayesian Kernel Machine Regression (BKMR) models, with three groups: PM size fractions (PM0.1, PM0.1-2.5, PM2.5-10), NO2, and O3. In BKMR models, the PM group was associated with ASD in year 2 (group posterior inclusion probability (gPIP) = 0.75), and marginally associated in year 1 (gPIP = 0.497). PM2.5-10 appeared to drive the association (conditional PIP (cPIP) = 0.64) in year 1, while PM0.1 appeared to drive the association in year 2 (cPIP = 0.76), with both showing a moderately strong increased risk. Pre-pregnancy O3 showed a slight J-shaped risk of ASD (gPIP = 0.55). No associations were observed for exposures during pregnancy. Pre-pregnancy O3 and year 2 p.m.0.1 exposures appear to be associated with an increased risk of ASD. Future research should examine ultrafine particulate matter in relation to ASD.

Variation of air pollutants during COVID-19 lockdown phases in the mega-city of Lahore (Pakistan); Insights into meteorological parameters and atmospheric chemistry

Variation of air pollutants during COVID-19 lockdown phases in the mega-city of Lahore (Pakistan); Insights into meteorological parameters and atmospheric chemistry

Monitoring gaseous pollutants using passive sampling in the Philadelphia region

ABSTRACT Air pollution can have deleterious impacts on human health and the environment. Historically, air pollution studies have focused more on cities. However, it is also important to consider the impact on large suburban populations living closer to the major cities. In this study, nitrogen oxides (nitrogen dioxide and nitric oxide), sulfur dioxide, ozone, and ammonia concentrations were measured from fifteen sites in the Greater Philadelphia area, Pennsylvania, USA using Ogawa passive samplers from September 2021 to May 2022. The fall season had the highest mean NOx concentrations (11.03 ± 4.51 ppb), and spring had the highest mean O3 concentration (18.65 ± 6.71 ppb) compared to other seasons. NOx concentrations were higher at suburban (30.43 ± 33.79 ppb) and urban sites (22.49 ± 12.54 ppb) compared to semi-rural sites (11.08 ± 9.20 ppb). SO2 was not detected in most of the measurements. The positive statistically significant correlation between NO and NH3 in urban (R2 = 0.33, p-value <0.05) and suburban sites (R2 = 0.37, p-value <0.05) during winter and spring, respectively, suggests a high attribution of traffic emissions to NH3 at urban and suburban sites. Influence of traffic emissions on air pollutant values for the study region is also supported by similar NOx concentrations between suburban and urban sites as well as decreasing NO2/NOx ratios with increased distance from expressways. This study shows that passive sampling can be effectively used for assessing spatial and seasonal variations in air pollutants within an area of diverse land use. Implications: This study presents the findings of temporal and seasonal patterns for nitrogen dioxide, nitric oxide, tropospheric ozone, and ammonia at urban, suburban, and semi-rural areas of the greater Philadelphia region. The main objective of the study is to monitor air pollution in suburban and semi-rural areas which are not monitored for air pollution. We monitored from a total of fifteen sites in three seasons to assess air pollution in suburban and semi-rural areas near the major city in the United States – Philadelphia. The findings are important to learn how air quality is affected in suburban and semi-rural areas near the major city. The study also shows the useful application of inexpensive passive sampling technique for measuring air pollution.

Air pollution trapping in the Dresden Basin from gray-zone scale urban modeling

Abstract. The microscale variability of urban air pollution is essentially driven by the interaction between meteorology and urban topography, which remains challenging to represent spatially accurately and computationally efficiently in urban dispersion models. Natural topography can additionally exert a considerable amplifying effect on urban background pollution, depending on atmospheric stability. This requires an equally important representation in models, as even subtle terrain-height variations can enforce characteristic local flow regimes. In this model study, the effects of urban and natural topography on the local winds and air pollution dispersion in the Dresden Basin in the Eastern German Elbe valley are investigated. A new, efficient urban microscale model is used within a multiscale air quality modeling framework. The simulations that consider real meteorological and emission conditions focus on two periods in late winter and early summer, respectively, as well as on black carbon (BC), a key air pollutant mainly emitted from motorized traffic. As a complement to the commonly used mass concentrations, the particle age content (age concentration) is simulated. This concept, which was originally developed to study hydrological reservoir flows in a Eulerian framework, is adapted here for the first time for atmospheric boundary-layer modeling. The approach is used to identify stagnant or recirculating orographic air flows and resulting air pollution trapping. An empirical orthogonal function (EOF) analysis is applied to the simulation results to attribute the air pollution modes to specific weather patterns and quantify their significance. Air quality monitoring data for the region are used for model evaluation. The model results show a strong sensitivity to atmospheric conditions, but generally confirm increased BC levels in Dresden due to the valley location. The horizontal variability of mass concentrations is dominated by the patterns of traffic emissions, which overlay potential orography-driven pollutant accumulations. Therefore, an assessment of the orographic impact on air pollution is usually inconclusive. However, using the age-concentration metric, which filters out direct emission effects, previously undetected spatial patterns are discovered that are largely modulated by the surface orography. The comparison with a dispersion simulation assuming spatially homogeneous emissions also proves the robustness of the orographic flow information contained in the age-concentration distribution and shows it to be a suitable metric for assessing orographic air pollution trapping. The simulation analysis indicates several air quality hotspots on the southwestern slopes of the Dresden Basin and in the southern side valley, the Döhlen Basin, depending on the prevailing wind direction.

Urban and suburban decadal variations in air pollution of Beijing and its meteorological drivers

Air pollution is a major threat to human health and ecosystems. Using 10-year (2013–2022) multi-source observations for the Beijing, China, we showed that clean-air actions have significantly reduced PM2.5, PM10, CO, NO2, and SO2 pollution, with an increase in the surface maximum daily 8-h average ozone (MDA8O3) concentrations during autumn and winter, leading to a rapid diminishment of the urban–suburban gap in air pollution. Secondary sources and vehicle emissions were enhanced in both urban and suburban areas in all seasons except summer from 2013 to 2022. By combining statistical analysis with the convergent cross-mapping model, the varying relationships between air pollution and meteorological conditions in the urban and suburban areas were delineated. The results suggested that boundary layer height and relative humidity exerted strong and stable influences on all air pollutants, except for MDA8O3, whose key meteorological driver was temperature. This study showed that increasing O3 trends in autumn and winter and aggravated O3 formation in summer in urban areas in Beijing became non-negligible from 2013 to 2022, despite the declining levels of air pollutants. Meteorological observations suggested that weather patterns in Beijing, characterized by higher temperatures, sunshine hours, and boundary layer height and lower relative humidity, have become more favorable for O3 formation in autumn and winter. Future mitigation efforts should focus on reducing VOC and NOx emissions to avoid further deterioration of O3 pollution under the frequent adverse meteorological conditions predicted under the background of global warming.

Comparison of NO2 and BC Predictions Estimated Using Google Street View-Based and Conventional European-Wide LUR Models in Copenhagen, Denmark

A widely used method for estimating fine scale long-term spatial variation in air pollution, especially for epidemiology studies, is land use regression (LUR) modeling using fixed off-road monitors. More recently, LUR models have been developed using data from mobile monitors that repeatedly measure road pollutants and mixed-effects modeling. Here, nitrogen dioxide (NO2) and black carbon (BC) predictions from two independent models were compared across streets (defined as 30–60 m road segments) (N = 30,312) and residences (N = 76,752) in Copenhagen, Denmark. The first model was Google Street View (GSV)-based mixed-effects LUR models (Google-MM) that predicted 2019 mean NO2 and BC levels, and the second was European-wide (EUW) LUR models that predicted annual mean 2010 levels at 100 m spatial resolution. Across street segments, the Spearman correlation coefficient between the 2019 NO2 from Google-MM-LUR and 2010 NO2 from EUW-LUR was 0.66, while at residences, this was 0.60. For BC, these were 0.51 across street segments and 0.40 at the residential level. The ratio of percentile 97.5 to 2.5 for NO2 across the study area streets using Google-MM NO2 was 4.5, while using EUW-LUR, this was 2.1. These NO2 ratios at residences were 3.1 using Google-MM LUR, and 1.7 using EUW-LUR. Such ratios for BC across street segments were 3.4 using Google-MM LUR and 2.3 using EUW-LUR, while at the residential level, they were 2.4 and 1.9, respectively. In conclusion, Google-MM-LUR NO2 for 2019 was moderately correlated with EUW-LUR NO2 developed in 2010 across Copenhagen street segments and residences. For BC, while Google-MM-LUR was moderately correlated with EUW-LUR across Copenhagen streets, the correlation was lower at the residential level. Overall, Google-MM-LUR revealed larger spatial contrasts than EUW-LUR.

Pollutant-meteorological factors and cardio-respiratory mortality in Portugal: Seasonal variability and associations

Seasonal variations in cardiorespiratory diseases may be influenced by air pollution and meteorological factors. This work aims to highlight the relevance of a complete seasonal characterization of the pollutant-meteorological factors and cardio-respiratory mortality in Portugal and the relationships between health outcomes and environmental risk factors. To this end, air pollution and meteorological variables along with health outcomes were analyzed at national level and on a monthly basis for the period of 2011–2020. It was found that cardiorespiratory mortality rates during winter were 44% higher than during the summer. Furthermore, particulate matter with aerodynamic diameters of 10 and 2.5 μm (μm) or smaller (PM10 and PM2.5), carbon monoxide (CO) and nitrogen dioxide (NO2) showed a seasonal variability with the highest concentrations during winter while ozone (O3) presented higher concentrations during spring and summer. PM10, PM2.5 and NO2, showed a positive correlation between seasons, indicating similar patterns of behavior. Canonical correlation analysis (CCA) applied to pollutant-meteorological and cardiorespiratory mortality data indicates a strong linear correlation between pollutant-meteorological factors and health outcomes. The first canonical correlation was 0.889, and the second was 0.545, both statistically significant (p-value < 0.001). The CCA results suggest that there is a strong association between near-surface temperature, relative humidity, PM10, PM2.5, CO and NO2 and health outcomes. The results of this study provide important information of the seasonal variability of air pollutants and meteorological factors in Portugal and their associations with cardiorespiratory mortality.

Spatiotemporal Variation and Pattern Analysis of Air Pollution and Its Correlation with NDVI in Nanjing City, China: A Landsat-Based Study

The rapid socio-economic development and urbanization in China have led to a decline in air quality. Therefore, the spatial and temporal distribution patterns of urban air pollution, as well as its formation mechanisms and influencing factors, have become important areas of research in atmospheric environment studies. This paper focuses on nine monitoring sites in Nanjing, where concentration data for six air pollutants and vegetation index data were collected from 2013 to 2021. The objective of this study is to investigate the changes in air pollutants and vegetation index over time and space, as well as their relationship with each other, and to assess the social and environmental impacts of air pollution. The findings reveal a spatial distribution pattern of air pollution in Nanjing that exhibits significant variability, with pollutant concentrations decreasing from the city center towards the surrounding areas. Notably, the main urban area has lower air quality compared to the peripheral regions. The results obtained from best-fit linear regression models and correlation heatmaps demonstrate a strong correlation (coefficient of determination, R2 &gt; 0.5) between the normalized difference vegetation index (NDVI) and pollutants such as SO2, NO2, PM2.5, PM10, and O3 within a radial distance of 2 km from the air pollutant monitoring sites. These findings indicate that NDVI can be an effective indicator for assessing the distribution and concentrations of air pollutants. Negative correlations between NDVI and socio-economic indicators are observed under relatively consistent natural conditions, including climate and terrain. Therefore, the spatiotemporal distribution patterns of NDVI can provide valuable insights not only into socio-economic growth but also into the levels and locations of air pollution concentrations.

Effects of air pollution on emergency visits for acute otitis media among children: a case-crossover study in Chongqing, China.

Many epidemiological studies have demonstrated the short-term effects of air pollution on acute otitis media (AOM) in children, but few studies have explored the association between AOM and air pollution in Chinese children. This study aimed to analyze the effects of air pollution on emergency visits for AOM among children through a time-stratified case-crossover design in Chongqing, China. The outpatient medical records of children from nine main urban districts who presented with AOM between December 22, 2018 and December 21, 2021 were collected from the Children's Hospital of Chongqing Medical University. Data for air pollution variables, including the air quality index (AQI), particulate matter ≤ 10 μm (PM10), PM2.5, SO2, CO, NO2 and O3 from 17 monitoring sites were collected. Data for meteorological factors as confounding variables also were collected. Conditional logistic regression was used to analyze the data with single-pollutant models, multi-pollutant models, and stratified analyses. Increases in AQI, PM10, PM2.5, SO2, CO and NO2 were positively associated with emergency visits for AOM among children in single-pollutant models and stratified analyses. Increases in PM10, SO2, CO and NO2 were positively associated with emergency visits for AOM among children in multi-pollutant models. NO2 had the most statistically significant OR values in all models, whereas significant effects of O3 were observed only in seasonal stratification. In single-pollutant models, we found that the best lag periods were lag 0-7 for air pollution variables except for O3 and the largest OR values were 1.185 (95%CI: 1.129-1.245) for SO2 in single-pollutant models. In stratified analyses, there were no difference between groups in these statistically significant OR values through gender and age stratification, while the differences between seasons in these OR values of PM10, SO2, CO, NO2 and O3 were statistically significant. Children aged 0 years and 3-5 years represented the most susceptible population, and among the seasons, susceptibility was greater during Winter and Spring. Short-term exposure to air pollution can increase emergency visits for AOM among children in Chongqing, China.

Burned agricultural biomass, air pollution and crime

Agricultural fires account for half as much burned biomass as forest fires. Like other forms of environmental degradation, burned biomass incurs social costs for society through its contribution to air pollution. One such potential cost is the increase in crime. Exploiting plausibly exogenous variation in air pollution due to increased rice stubble burning in Pakistan, I provide evidence on the causal relationship between air pollution and crime. Air pollution increases all crimes, but the increase in violent crimes is particularly salient. I provide evidence that a reduction in earnings for middle-aged and older male individuals is an important potential mechanism. I also discuss the potential psychological mechanisms involved. The social costs of burned agricultural biomass are much wider and have significant implications for developing economies that rely on agriculture.

Variability of traffic-related air pollutants at two- and four-phase intersections

Traffic intersections are the hotspots of air pollution. However, pollutant variation patterns at signalized intersections remain less explored. In this study, field measurements were conducted to investigate the pollutant variability at two- and four-phase intersections in Beijing, China. Spikes were frequently observed in pollutant concentrations at a fine-temporal (1-HZ) scale that could be well-characterized using Gaussian peak functions. The mean pollutant concentrations at two-phase intersections peaked under the green signal light and decreased exponentially under the red signal light. Pollutant concentrations were significantly influenced by the signal control pattern, which higher pollutant concentrations were observed during the pedestrian-waiting phase at the two-phase intersection and during the cross-walking phase at the four-phase intersection. Additionally, intersections with shorter signal light times result in insufficient pollutant dispersion and more frequent stop-and-go by vehicles. Nevertheless, the traffic volume is the dominant factor for a higher pollution level. These results broaden our knowledge of pollutant variability at signalized intersections and have implications for the mitigation of traffic-related air pollution.

Impacts of urban expansion on meteorology and air quality in North China Plain during wintertime: A case study

The North China Plain (NCP) is one of the areas with a fast urbanization rate. The proportion of urban areas has increased from 1.78% in 2000 to 6.70% in 2015 in the NCP. Urban expansion induce variations in air pollutants are mainly determined by changes in meteorological conditions and atmospheric physicochemical processes. A persistent severe wintertime particulate pollution episode is simulated using the WRF-Chem model to investigate the impacts of land use/cover change on air quality in the NCP. Sensitivity simulations demonstrated that the urban expansion increases the near-surface temperature by approximately 0.2 °C, and further the development of the planetary boundary layer height by approximately 1.6%, but decreases the relative humidity by 0.4% on average. The near-surface O3 concentrations are increased by 2.2%, but the PM2.5 concentrations are decreased by 2.4% on average in the NCP. The near-surface concentration of NO2, SO2, and CO decrease by 4.5%, 2.2%, and 2.7%, respectively. The decrease in PM2.5 concentrations is approximately 8.9% in urban areas. Although urban expansion increases anthropogenic emissions of air pollutants, it alleviates particulate pollution to some degree, particularly in urban areas.

Ambient carbon monoxide and relative risk of daily hospital outpatient visits for respiratory diseases in Lanzhou, China.

At present, evidence of the associations between carbon monoxide (CO) and respiratory diseases (RD) in Northwest China is limited and controversial. The aim of this study is to evaluate the impact of ambient CO on outpatient visits for RD in Lanzhou, China. The daily amount of outpatient visits for total and cause-specific RD, air pollutant, and weather variables were collected in Lanzhou, China from 1st January 2013 to 31st December 2019. A generalized additive model and distributed lag nonlinear model were used to assess associations between CO and outpatient visits for RD. During the study period, a total of 1,623,361 RD outpatient visits were recorded. For each interquartile range (IQR) (0.77mg/m3) increase in CO, the relative risk (RR) was 1.163 (95% CI: 1.138, 1.188) for total RD at lag07, 1.153 (95% CI: 1.128,1.179) for upper respiratory tract infection (URTI) at lag07, 1.379 (95% CI: 1.338,1.422) for pneumonia at lag07, 1.029 (95% CI: 0.997,1.062) for chronic obstructive pulmonary disease (COPD) lag04, 1.068 (95% CI: 1.028,1.110) for asthma lag03, and 1.212 (95% CI: 1.178,1.247) for bronchitis lag07, respectively. In the subgroup analyses, the impacts of CO were more pronounced on total RD, pneumonia, COPD, and bronchitis in males than females, while the opposite was true in URTI and asthma. The impact of CO on RD was the strongest for children under 15years-of-age. We also found significantly stronger effects during cold seasons compared to warm seasons. In addition, we observed a roughly linear exposure-response curve between CO and RD with no threshold effect. This study in Lanzhou revealed a remarkable association between CO level and an elevated risk of total and cause-specific RD outpatient visits, especially for pneumonia.

Air pollution and motor vehicle collisions in New York city

Road traffic accidents are a pervasive feature of everyday life, killing 36,500 people, injuring 4.5 million and, overall, generating costs to the American society of $340 billion in 2019. Understanding the underlying factors can improve the design of prevention strategies. We use all road traffic collisions in New York City between 2013 and 2021 (N = 1,269,600) and match each individual collision to the nearest weather and air pollution station. Our study uses highly disaggregated data using an hourly frequency of collisions at a fine spatial level incorporating various air pollutants and weather factors. We employ an instrumental variable approach using temperature inversions to provide exogenous variation in air pollution addressing endogeneity and measurement error concerns. We find that higher concentrations of carbon monoxide (CO) and sulfur dioxide (SO2) increase the number of collisions but leave the severity (persons injured or killed) unaffected. Part of this can be explained by the effect of air pollutants on aggressive behavior: CO (p < .05) and SO2 (p < .01) increase the number of collisions caused by aggressive driving. Interestingly, this channel is only present in male drivers. Our results provide additional evidence that air pollution not only adversely affects health, but also has “non-health” related effects which are costly for the society.

Influence of air pollution and climate variability on dengue in Singapore – A time-series analysis

Influence of air pollution and climate variability on dengue in Singapore – A time-series analysis

Estimation of Air Pollutant Emissions by Tractor Utilization in Korea

The utilization of tractors is essential because of both the shortage of agricultural labor forces and the rapid aging of farmers in Korean agriculture. Tractors can be classified into two types: a walking tractor called as a power tiller, and a riding tractor. In this study, diesel emission inventories of tractors were established and variations of air pollutants were analyzed with each other using 2011 and 2019 survey data in Korea. Agricultural tractors including walking and riding types, which were categorized into three levels via the rated output power, were the main sources of diesel emissions. The emission inventory including CO, NOx, SOx, TSP(PM10), PM2.5, VOCs, and NH3 were established using a Tier 3 methodology. The total amount of emissions using agricultural tractors was decreased by about 13% from 2011 to 2019. The number of walking tractors were decreased by about 19% in 8 years; on the other hand, that of riding tractors were increased by about 12%. However, the emission reduction is about 48% for walking tractors, and the emission increment is about 5% for riding tractors. Thus, the total emission from agricultural tractors was decreased by about 16% in those periods. It is due to the decrease of 21% and 15% in the hours of use of walking and riding tractors, respectively, in 2019. Walking tractors mainly emit air pollutants from spraying and transporting. Riding tractors emit about 61% of the total air pollutants mainly from soil preparation and transporting operations. The geographic information system (GIS) was used to visualize the distribution of air pollutants in Korea. High-emission generating regions and the changes of emissions over 8 years were clearly seen in the GIS analysis. High air-pollutant emitting regions are mainly located in the western and southern regions of Korea, which have plenty of arable areas compared to other regions in Korea.

Data-Driven Placement of PM2.5 Air Quality Sensors in the United States: An Approach to Target Urban Environmental Injustice.

In the United States, citizens and policymakers heavily rely upon Environmental Protection Agency mandated regulatory networks to monitor air pollution; increasingly they also depend on low-cost sensor networks to supplement spatial gaps in regulatory monitor networks coverage. Although these regulatory and low-cost networks in tandem provide enhanced spatiotemporal coverage in urban areas, low-cost sensors are located often in higher income, predominantly White areas. Such disparity in coverage may exacerbate existing inequalities and impact the ability of different communities to respond to the threat of air pollution. Here we present a study using cost-constrained multiresolution dynamic mode decomposition (mrDMDcc) to identify the optimal and equitable placement of fine particulate matter (PM2.5) sensors in four U.S. cities with histories of racial or income segregation: St. Louis, Houston, Boston, and Buffalo. This novel approach incorporates the variation of PM2.5 on timescales ranging from 1day to over a decade to capture air pollution variability. We also introduce a cost function into the sensor placement optimization that represents the balance between our objectives of capturing PM2.5 extremes and increasing pollution monitoring in low-income and nonwhite areas. We find that the mrDMDcc algorithm places a greater number of sensors in historically low-income and nonwhite neighborhoods with known environmental pollution problems compared to networks using PM2.5 information alone. Our work provides a roadmap for the creation of equitable sensor networks in U.S. cities and offers a guide for democratizing air pollution data through increasing spatial coverage of low-cost sensors in less privileged communities.