Air Pollution Exposure Risk Research Articles

Modelling PM2.5 for Data-Scarce Zone of Northwestern India using Multi Linear Regression and Random Forest Approaches

ABSTRACT PM2.5 (Particulate matter with aerodynamic diameter <2.5 m) concentrations above permissible limit causes air quality deterioration and hampers human health. Due to the lack of a good spatial network of ground-based PM monitoring sites and systematic checking, the availability of continuous data of PM2.5 concentrations at macro and meso scales is restricted. Present research estimated PM2.5 concentrations at high (1 km) resolution over Faridabad, Ghaziabad, Gurugram and Gautam Buddha Nagar, a data-scarce zone of the highly urbanized area of northwestern India for the year 2019 using Random Forest (RF), Multi-Linear Regression (MLR) models and Hybrid Model combining RF and MLR. It included Aerosol Optical Depth (AOD), meteorological data and limited in-situ data of PM2.5. For validation, the correlation coefficient (R), Root-Mean-Square Error (RMSE), Mean Absolute Error (MAE) and Relative Prediction Error (RPE) have been utilized. The hybrid model estimated PM2.5 with a greater correlation (R = 0.865) and smaller RPE (22.41%) compared to standalone MLR/RF models. Despite the inadequate in-situ data, Greater Noida has been found to have a high correlation (R = 0.933) and low RPE (32.13%) in the hybrid model. The most polluted seasons of the year are winter (137.28 µgm−3) and post-monsoon (112.93 µgm−3), whereas the wet monsoon (44.56 µgm−3) season is the cleanest. The highest PM2.5 level was recorded in Noida followed by Ghaziabad, Greater Noida and Faridabad. The findings of the present research will provide an input dataset for air pollution exposure risk research in parts of northwestern India with sparse monitoring data.

Investigating the vertical distribution patterns of urban air pollution based on unmanned aerial vehicle gradient monitoring

• Vertical distributions of urban pollution were investigated by instrumented UAVs. • A west-east sampling gradient belt of the built-up area was used for field monitoring. • The concentrations of particulate matter decreased from west to east on Days 2 and 3. • Ground cumulative effects of PM 1 , PM 2.5 and PM 10 were higher than those of SO 2 and NO 2 . Understanding the vertical distribution patterns of air pollution is crucial to elucidate the formation mechanism of extreme air pollution events and explore the air pollution exposure risks of residents. The vertical air pollutant (SO 2 , NO 2 , PM 1 , PM 2.5 and PM 10 ) concentrations along a west-east sampling belt of 0–120 m height were investigated using a multirotor unmanned aerial vehicle (UAV) equipped with mobile sensors on 4 monitoring days. Vertical distribution patterns were explored by ordinary least-squares regression and Pearson correlation coefficient. The results indicated that the concentration of particulate matter decreased gradually from west to the east (S1 to S8) on monitoring Days 2 and 3. The ground cumulative effects of atmospheric particulate matter (PM 1 , PM 2.5 , PM 10 ) were significantly higher than those of gaseous pollutants (SO 2 , NO 2 ). The vertical variation ranges of pollutants from large to small were PM 10 ( k = 0.18), PM 2.5 ( k = 0.16), PM 1 ( k = 0.07), NO 2 ( k = 0.06) and SO 2 ( k = 0.01). Atmospheric particulate matter tends to change significantly with vertical height, and the concentration decreases gradually with increasing height. The proposed UAV based gradient monitoring approach and vertical pollutant change trend analysis method could promote the urban air pollution researches and urban sustainable development.

Predicting the least air polluted path using the neural network approach

Air pollution exposure during daily transportation is becoming a critical issue worldwide due to its adverse effect on human health. Predicting the least air polluted healthier path is the best alternative way to mitigate personal air pollution exposure risk. Computing the least polluted path for th

Assessment of Air Pollution Impacts and Monitoring Data Limitations of a Spring 2019 Chemical Facility Fire

Fires, explosions, and spills at petrochemical industry facilities create air pollution exposure risks for surrounding residences, which are disproportionately communities of color and low-income c...

Ridership exceedance exposure risk: Novel indicators to assess PM2.5 health exposure of bike sharing riders

Identifying the fine particulate matter (PM2.5) exposure risk for bicycle riders is crucial for promoting the development of theory and technology in transportation-related air pollution assessment as well as urban health planning. Previous studies have employed daily mean PM2.5 concentrations and designed routes to evaluate air pollution exposure risk. However, because the daily mean PM2.5 concentrations cannot fully illustrate the intra-day variations in PM2.5, which are typically higher than daily mean values, the adverse effects of PM2.5 concentrations remain underestimated. Moreover, the quantity and representativeness of monitoring samples make large spatial-scale and multi-temporal-scale analysis challenging. By defining hourly exceedance PM2.5 concentration and sharing bicycle rider data, two novel indicators were proposed in our study: exceedance exposure risk of PM2.5 for sharing bicycle riders (EPSR) and accumulative exceedance exposure risk of PM2.5 for sharing bicycle riders (AEPSR). Standard deviation ellipse analysis was conducted to investigate the multi-temporal variation of ESPR and AEPSR. A geographically weighted regression model was applied to quantify the relationship between city function zones and exceedance PM2.5 exposure risk for sharing bicycle riders. Results revealed that the mean values of EPSR and AEPSR during morning peak periods ranged between 0.109 min μg/m3 and 1.27 min μg/m3 and 6.83 min μg/m3 and 43.41 min μg/m3, respectively, whereas the mean values of EPSR and AEPSR during evening peak periods ranged between 0.19 min μg/m3 and 4.28 min μg/m3 and 14.67 min μg/m3 and 357.66 min μg/m3, respectively. This implied that sharing bicycle riders were exposed to higher PM2.5-related risks during the evening than in the morning. When considering the accumulative effects, the average centers of the AEPSR moved to the north side as compared to the average centers of the EPSR. Expanding areas of EPSR shrunk by 20.25 km2. This indicated that accumulative effects aggregated spatial clusters of exceedance PM2.5 exposure risk for sharing bicycle riders more tightly to the north of the study areas. Spatiotemporal variation of EPSR and AEPSR led us to investigate the mechanism behind this phenomenon. Spatial associations between city function zones and EPSR and AEPSR showed that sharing bicycle riders experienced more severe exceedance PM2.5 exposure risk around financial/corporations and leisure service areas, with R2 values of 0.33 and 0.35, respectively. This spatial association tended to be more significant during the evening peak periods. By developing two novel indicators, the increasing health threats for bicycle riders caused by exceedance PM2.5 were investigated in this study. The mechanism results should be included for developing mitigation strategies to alleviate the adverse effects of air pollution for public rider participators and achieving the goal of eco-health cities.

Exploring spatiotemporal variation characteristics of exceedance air pollution risk using social media big data

Air pollution in the form of PM2.5 decreases life expectancy considering its contribution to morbidity and mortality. Therefore, scientific and accurate PM2.5 exposure risk assessment is essential. However, the considering daily/hourly mean PM2.5 concentration and overlooking population mobility in exposure risk assessments result in underestimation of its adverse effects. Thus, using social media data and exceedance PM2.5 concentration, two novel indicators named hourly exceedance PM2.5 exposure (HEPE) and daily cumulative variation of exceedance PM2.5 exposure (DCEPE) are developed in our study. Spatiotemporal variation analysis of HEPE showed that the first exceedance PM2.5 exposure risk was observed at 10:00; this lasted till the end of the day. According to the standard deviation ellipse (SDE) method analysis results, at first, the major spatial tendency direction was northeast-southwest, with the average center (AE) located in the Yuangang Township. Then, the HEPE in the western study area increased dramatically. The major spatial tendency direction shifted from northeast-southwest to east-west. Consequently, the AE of HEPE shifted to central study area. The spatiotemporal variation characteristics led us to investigate the mechanisms. A bivariate LISA was applied to detect the spatial association between DCEPE and city functional zones (CFZs). Results showed that highly spatial associations were found between the DCEPE and CFZs in the southwest of the study area. Residential neighborhoods and transportation services showed a closer relationship with the spatial distribution of DCEPE. Based on these results, we found increasing public health threats posed by PM2.5. Thus, HEPE is an essential factor to assess air pollution exposure risk. Moreover, more attention should be paid to the spatial association between DCEPE and city functions, which is important for the development of air pollution mitigation strategies.

Hazardous air pollutant exposure as a contributing factor to COVID-19 mortality in the United States

To date, COVID-19 has claimed more than 100 000 American lives. Early inquiry suggests preexisting conditions are key risk factors contributing to COVID-19 mortality and air pollution exposure could exacerbate this relationship. Building on prior research linking deaths from respiratory viruses to air pollution exposures, we investigate how 2014 National Air Toxics Assessment hazardous air pollutants (HAPs) respiratory hazard quotient and respiratory hazard index are related to COVID-19 mortality. Our focus on HAPs builds upon the knowledge base linking poor air quality to COVID-19 mortality, since most (if not all) earlier studies only include criteria pollutants. Herein, we examine the relationship between HAP exposure and US-based COVID-19 mortality, while controlling for socioeconomic status, population health indicators, and exposure to PM2.5 and ozone. We fit county-level negative binomial mixed models, predicting COVID-19 mortality as a function of HAP respiratory toxicity levels and relevant covariates. We include models for combined exposure to HAPs, as well as for specific pollutants. We find that an increase in the respiratory hazard index is associated with a 9% increase in COVID-19 mortality. Although differing in magnitude, this association holds for individual HAPs acetaldehyde, and diesel PM. These findings help us to understand variation in US-based COVID-19 mortality rates, reinforce existing research linking air pollution to mortality, and emphasize the importance of regulatory efforts to limit air pollution exposure risk.

Efforts in reducing air pollution exposure risk in China: State versus individuals

China has made great efforts towards air pollutant concentration control during the past five years, which has led to positive outcomes. However, air pollutant concentration focused efforts were considered separately from human exposure risk. And this might result in a misunderstanding that reducing exposure risk can only rely on the national level measures of air pollutant control. This study integrates the first Chinese survey of human activity patterns and the spatially continuous high-resolution PM2.5 concentration maps to reveal the spatial and temporal variations of China’s air pollution exposure risk from 2013 to 2017. More importantly, the effects on risk reduction from multi-scale and multi-object perspectives (reductions of ambient PM2.5 concentrations by national or provincial measures and changes of individual behavior patterns by personal efforts) are deeply investigated. Results show that the reductions of PM2.5 concentration and associated reductions of exposure risk from 2013 to 2017 were 40% and 35.7%, respectively. They also showed that both the reduction of PM2.5 concentrations and change of personal behavior patterns were effective for risk reduction when China’s total PM2.5 exposure risk was higher than 1.58. However, only individual behavior changes contributed to risk reduction for scenarios with state-level risk value below 1.58. For regional strategies, threshold values for PM2.5 exposure risk control differentiating national measures or personal efforts were spatially and temporally dependent. The role of personal behavior changes on PM2.5 exposure risk reduction was growing in these five years with concentration rapidly decreasing regions. The findings suggest that people-centered air pollution exposure risk prevention not only depends on government management for air pollution control, but also on individual changes of activity patterns. Efforts from the state and individuals are both essential for reducing air pollution exposure risk in China, especially growing individual efforts are needed in regions with the decreasing air pollutant concentrations in the coming future. Moreover, this study mainly discussed the PM2.5 exposure risk from the macroscopic perspective, the research at the microcosmic perspective is also needed in the further study.

Healthier routes planning: A new method and online implementation for minimizing air pollution exposure risk

Air pollution exposure during daily traveling is growing as an increasingly serious factor affecting public health with rapid incensement of travel distance in urban sprawl. Finding a healthier route with least exposure risk might be an alternative way to mitigate adverse health outcomes under the truth that worldwide air pollution in urban area cannot be eliminated within a short period of time. Integrating techniques of fine scale mapping of air pollutant concentration, risk weight estimation of road segment exposure to air pollutants, and dynamic Dijkstra algorithm capable of updating route, this study for the first time proposes a healthier route planning (HRP) method to minimize personal travel exposure risk to air pollution. Effectiveness of HRP in mitigating exposure risk was systematically tested based on hundred pairs of origins and destinations located in Beijing-Tianjin-Hebei (BTH) of China with necessarily dense air quality observations. Results show that the spatiotemporal variations of air pollutant concentrations were significant and these differences indeed occurred with time at hourly scale. Meanwhile, the grid-based estimation of exposure risk is time dependent with risk ranging from 5 to 109, which echoes the necessity of healthier route planning. Compared to routes with the shortest distance and least travel time, healthier route has the least exposure risk. And this risk mitigation effect is more significant in areas with wide exposure risk variations than those in areas without obvious risk difference over space (e.g., 21.38% vs. 0.86%). Results suggest that HRP method is promising to minimize personal exposure risk during daily travel based on the accurate exposure risk estimation of road segment at high spatiotemporal resolution. This role could be more important in areas with longer travel distance and greater heterogeneous distribution of air pollution in great metropolis.

Health economic assessment of a shift to active transport

Active transportation (walking or cycling) as a substitute for car trips still represents a small percentage of all daily travels in many European cities. This study aimed to estimate the health and economic co-benefits for the adult population of modal shift from driving to active travel in urban environments. Three scenarios were modelled for the case study, the city of Porto, Portugal, by comparing travel patterns of 2013 to hypothetical scenarios of modal shifts from driving to active transport, namely: i) SC1 - conservative scenario, with a change of 5% from driving to cycling and 10% from driving to walking; ii) SC2 - moderate scenario, with a shift of 10% and 15%, respectively; and iii) SC3 - optimistic scenario, with a shift of 15% and 20%, respectively. The mortality risk reduction for five health outcomes (colon and breast cancers, diabetes, ischemic heart disease, cerebrovascular disease) was assessed, including an estimation of traffic injury and air pollution exposure risks. Results were presented in Disability-Adjusted Life Years (DALYs) avoided. Economic valuation for each scenario was performed using a Willingness-to-Pay approach for morbimortality and a Cost of Illness approach for 2013 hospitalizations and work absenteeism. Significant health benefits were found in all modelled scenarios, ranging from 1657 (16%) to 2881 (28%) DALYs avoided. Total costs averted ranged from €3894 to €6769 million through the scenarios. Cardio and cerebrovascular diseases mortality presented the largest benefit, accounting for about 3/4 of all avoidable DALYs in all scenarios. Reductions in CO2 and PM10 emissions were calculated, showing a decrease from 31.6 to 73.7 kt of CO2 and 7 to 16 t for PM10, respectively. A modal shift towards active transportation could lead to significant health and economic benefits, indicating that the evaluation of health impacts should be included in the analysis of active transport interventions.

Chlorinated dibenzo-p-dioxin and dibenzofuran congener and homologue distributions in tree bark from Sauget, Illinois, U.S.

Polychlorinated dibenzo-p-dioxins (PCDD) and dibenzofurans (PCDF) are ubiquitous urban/industrial contaminants found in tree bark, which acts as a long-term passive atmospheric sampler. Twenty seven bark samples (tree age 8–92 years) were collected from residential and industrial areas near Sauget, IL to identify the trends of 2,3,7,8-Cl PCDD & PCDF congener distributions, toxic equivalents (TEQ), and PCDD & PCDF homologue distributions. The Sauget area is heavily industrialized, with a long history of chlorine production, and is the largest single contributor to air pollution exposure risk in St. Louis. Analysis of seven 2,3,7,8 Cl-substituted PCDD and 10 PCDF congeners showed ∑PCDD7 ranging from 2214 to 71821 pg g(–1) lipid and ∑PCDF10 from 355 to 13707 pg g(–1) lipid, the highest in trees <20 years old in both cases. OctaCCD and octaCDF respectively dominated ∑PCDD7 (89% average) and ∑PCDF10 (57% average). The 2,3,7,8-Cl congener profiles showed slight differences among samples showing that all were affected by similar sources but at different magnitudes. ∑TEQ ranged from 35 to 624 pg g(–1) lipid, dominated by 1,2,3,7,8-PeCDD (average = 41.3% of total TEQ). Tetra-Cl through hepta-Cl homologues were dominated by non-2,3,7,8-Cl compounds for both CDD and CDF. Homologue profiles for 26 samples had a “sink” profile dominated by octaCDD, whereas one sample showed effects of a local source.

Performance of Kriging and EWPM for Relative Air Pollution Exposure Risk Assessment

This study investigates the effectiveness of the Kriging interpolation model and the Emission Weighted Proximity Model (EWPM) in assessing relative exposure risk of air pollution using results from the American Meteorological Society/EPA Regulatory Model (AERMOD) as benchmarks. We used simulated exposure risk to SO 2 in the Dallas area in Texas in this evaluation. Results suggest that the relative exposure risks to SO 2 at different locations in the study area as estimated by EWMP are closer to estimated risks from AERMOD when compared with the results calculated by Kriging. In addition, study results also indicate that the relative exposure risks calculated by Kriging are similar to those from AERMOD when the density of emission sources in the area in question is high. It is therefore concluded that relative exposure risks determined by both the Kriging interpolation method and the EWPM are acceptable when it is not possible to use AERMOD. In situations when the density of emission sources is low in the study area, EWPM is a better choice than Kriging.

A Web-Based Personalized Mobility Service for Smartphone Applications

Nowadays, most of the basic web services use instant location information for providing suitable content to smartphone users. However, more intelligent smartphone applications such as context-based search and advertising, early warning systems and city-wide sensing applications may require additional information about smartphone users such as their mobility profiles. To meet more personalized demand of these applications we propose TRACK ME: A new web-based framework for smartphone applications with personalized lightweight mobility service as well as location tracking and mobility profile construction. We showed that our personalized mobility service supports different smartphone applications and it is lightweight enough to provide fast access to the mobility profiles of smartphone users. We illustrate the benefits of our mobility service on two smartphone applications: location prediction and air pollution exposure risk estimation.

Skewed Riskscapes and Environmental Injustice: A Case Study of Metropolitan St. Louis

This article presents a case study of Toxics Release Inventory (TRI) air pollution exposure risks across metropolitan St. Louis. The first section critically reviews environmental justice research and related barriers to environmental risk management. Second, the paper offers a conventional analysis of the spatial patterns of TRI facilities and their surrounding census block group demographics for metropolitan St. Louis. Third, the article describes the use of an exposure risk characterization for 319 manufacturers and their air releases of more than 126 toxic pollutants. This information could lead to more practical resolutions of urban environmental injustices. The analysis of TRIs across metropolitan St. Louis shows that minority and low-income residents were disproportionately closer to industrial pollution sources at nonrandom significance levels. Spatial concentrations of minority residents averaged nearly 40% within one kilometer of St. Louis TRI sites compared to 25% elsewhere. However, one-fifth of the region's air pollution exposure risk over a decade was spatially concentrated among only six facilities on the southwestern border of East St. Louis. This disproportionate concentration of some of the greatest pollution risk would never be considered in most conventional environmental justice analyses. Not all pollution exposure risk is average, and the worst risks deserve more attention from environmental managers assessing and mitigating environmental injustices.