Underestimated or overestimated? Dynamic assessment of hourly PM2.5 exposure in the metropolitan area based on heatmap and micro-air monitoring stations

High-resolution monthly assessment of population exposure to PM2.5 and its relationship with socioeconomic activities using multisource geospatial data

Understanding the spatiotemporal dynamics of population exposure to PM2.5 (PEP) and its relationship with socioeconomic activity (SEA) is crucial to reduce exposure risks and health dangers. However, few studies have investigated the dynamic variations of PEP within large regions at high spatiotemporal resolution; further, the impact mechanism between PEP and SEA remains largely unclear. Therefore, we estimated highly accurate PM2.5 concentrations in the Hunan province, China, using the Boruta and random forest (RF) algorithms and evaluated high-spatiotemporal-resolution PEP based on the estimated PM2.5 and obtained population data. Nighttime light data were used as a proxy of SEA to analyze the relationship between PEP and SEA. The results revealed that the Boruta-RF model predicted PM2.5 with fewer errors than the RF and stepwise multiple linear regression models, with the mean root-mean-square error reduced by 6.18% and 11.15%, respectively. The monthly PM2.5 concentrations in 2015 showed a U-shaped curve, with the entire provincial population exposed to monthly mean concentrations > 15 μg/m3. Heavier PM2.5 pollution tended to occur in densely populated areas, particularly in winter months. Using both fine-scale PM2.5 and population data improved the reliability of monthly PEP assessments and avoided over- and under-responses. Moreover, the PEP risk exhibited a unimodal structure, with a peak in January, at which point the urban-rural difference in PEP was the greatest. Further, PEP was positively influenced by SEA, with clear spatial spillover effects. SEA had an active impact on PEP during festivals and holidays, with the greatest consistency between the two occurring in November. These findings provide crucial insights for managing PM2.5 pollution.

Short-term exposure to PM2.5 pollution in Iran and related burden diseases

The objective of this study was to estimate the health effects attributed to PM2.5 exposure in southwest of Iran. In order to estimate HA-CVD, HA-RD, LC-M, I-As in children, RAD, and WDL, the exposure-response function method was used. The annual mean of PM2.5 regularly exceeded 5.26-8.5 times from 2021 annual limit value established by the WHO. The dominance of PM2.5 in PM2.5/PM10 ratio decreased -34.6% from 2015 to 2020.  The results showed that the risks of HA-CVD (- 51. 9), HA-RD (- 68.7%), LC-M (- 43.6%), I-As (- 52.1%), RAD (- 56.8%), and WDL (- 58.7%) declined per 105 inhabitants between 2018 and 2020 . Reducing the particulate emissions from industries and road traffic led to lower exposure to PM2.5, which will be effective in decrease of mortality rate.

Explainable geospatial-artificial intelligence models for the estimation of PM2.5 concentration variation during commuting rush hours in Taiwan

PM2.5 concentrations are higher during rush hours at background stations compared to the average concentration across these stations. Few studies have investigated PM2.5 concentration and its spatial distribution during rush hours using machine learning models. This study employs a geospatial-artificial intelligence (Geo-AI) prediction model to estimate the spatial and temporal variations of PM2.5 concentrations during morning and dusk rush hours in Taiwan. Mean hourly PM2.5 measurements were collected from 2006 to 2020, and aggregated into morning (7 a.m.-9 a.m.) and dusk (4 p.m.-6 p.m.) rush-hour mean concentrations. The Geo-AI prediction model was generated by integrating kriging interpolation, land-use regression, machine learning, and a stacking ensemble approach. A forward stepwise variable selection method based on the SHapley Additive exPlanations (SHAP) index was used to identify the most influential variables. The performance of the Geo-AI models for morning and dusk rush hours had accuracy scores of 0.95 and 0.93, respectively and these results were validated, indicating robust model performance. Spatially, PM2.5 concentrations were higher in southwestern Taiwan for morning rush hours, and suburban areas for dusk rush hours. Key predictors included kriged PM2.5 values, SO2 concentrations, forest density, and the distance to incinerators for both morning and dusk rush hours. These PM2.5 estimates for morning and dusk rush hours can support the development of alternative commuting routes with lower concentrations.

Multi-scenario PM2.5 distribution and dynamic exposure assessment of university community residents: Development and application of intelligent health risk management system integrated low-cost sensors

Exposure scenario and receptor behavior significantly affect PM2.5 exposure quantity of persons and resident groups, which in turn influenced indoor or outdoor air quality & health management. An Internet of Things (IoT) system, EnvironMax+, was developed to accurately and conveniently assess residential dynamic PM2.5 exposure state. A university community "QC", as the application area, was divided into four exposure scenarios and five groups of residents. Low-cost mobile sensors and indoor/outdoor pollution migration (IOP) models jointly estimated multi-scenario real-time PM2.5 concentrations. Questionnaire was used to investigate residents' indoor activity characteristics. Mobile application (app) "Air health management (AHM)" could automatic collect residents' activity trajectory. At last, multi-scenario daily exposure concentrations of each residents-group were obtained. The results showed that residential exposure scenario was the most important one, where residents spend about 60 % of their daily time. Closing window was the most significant behavior affecting indoor contamination. The annual average PM2.5 concentration in the studied scenarios: residential scenario (RS) < public scenario (PS) < outdoor scenario (OS) < catering scenario (CS). Except for CS, the outdoor PM2.5 in other scenarios was higher than indoor by 5-10 μg/m3. The multi-scenario population weighted annual average exposure concentration was 37.1 μg/m3, which was 78 % of the annual average outdoor concentration. The exposure concentration of 5 groups: cooks > outdoor workers > indoor workers > students > the elderly, related to their daily activity time proportion in different exposure scenario.

Exposed to NO2 in the center, NOx polluters in the periphery: Evidence from the Paris region

Air pollution is the cause of many health problems. In cities, combustion vehicles are a major contributor to emissions of key air pollutants. While many studies have focused on populations exposed to pollutants and the resulting environmental and social inequalities, few compare exposures and contributions. In this research, the population of the Household Travel Survey of the Paris region is studied by confronting two elements: the average individual exposure to NO₂ during an average working day and the average traffic NO_x emitted during a day by the motorized trips for each resident surveyed. The dynamic exposure to NO₂ of each resident is estimated according to activities in an average working day. The results confirm an environmental inequality according to the place of residence: on average, the center residents contribute little to pollutant emissions but are highly exposed. Some categories of the population, including women and the socially disadvantaged, are the most affected by these inequalities.

Temporal and Spatial Heterogeneity of PM2.5 Related to Meteorological and Socioeconomic Factors across China during 2000-2018

In recent years, air pollution caused by PM_2.5 in China has become increasingly severe. This study applied a Bayesian space-time hierarchy model to reveal the spatiotemporal heterogeneity of the PM_2.5 concentrations in China. In addition, the relationship between meteorological and socioeconomic factors and their interaction with PM_2.5 during 2000-2018 was investigated based on the GeoDetector model. Results suggested that the concentration of PM_2.5 across China first increased and then decreased between 2000 and 2018. Geographically, the North China Plain and the Yangtze River Delta were high PM_2.5 pollution areas, while Northeast and Southwest China are regarded as low-risk areas for PM_2.5 pollution. Meanwhile, in Northern and Southern China, the population density was the most important socioeconomic factor affecting PM_2.5 with q values of 0.62 and 0.66, respectively; the main meteorological factors affecting PM_2.5 were air temperature and vapor pressure, with q values of 0.64 and 0.68, respectively. These results are conducive to our in-depth understanding of the status of PM_2.5 pollution in China and provide an important reference for the future direction of PM_2.5 pollution control.