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# Distribution patterns and influencing factors of population exposure risk to particulate matters based on cell phone signaling data

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... For example, Liu et al. (2020) scrutinized the association between urban land use and commuting flows. Zhang et al. (2023) assessed population exposure risk to particulate matter. Zhang et al. (2020) analyzed the megaregional structure in the Pearl River Delta, China. ...
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Significance Severe haze events with large temporal/spatial coverages have occurred frequently in wintertime northern China. These extremes result from a complex interplay between emissions and atmospheric processes and provide a unique scientific platform to gain insights into many aspects of the relevant atmospheric chemistry and physics. Here we synthesize recent progress in understanding severe haze formation in northern China. In particular, we highlight that improved understanding of the emission sources, physical/chemical processes during haze evolution, and interactions with meteorological/climatic changes are necessary to unravel the causes, mechanisms, and trends for haze pollution. This viewpoint established on the basis of sound science is critical for improving haze prediction/forecast, formulating effective regulatory policies by decision makers, and raising public awareness of environmental protection.
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Air pollution imposes significant environmental and health risks worldwide and is expected to deteriorate in the coming decade as cities expand. Measuring population exposure to air pollution is crucial to quantifying risks to public health. In this work, we introduce a big data analytics framework to model residents' stay and commuters' travel exposure to outdoor PM2.5 and evaluate their environmental justice, with Beijing as an example. Using mobile phone and census data, we first infer travel demand of the population to derive residents' stay activities in each analysis zone, and then focus on commuters and estimate their travel routes with a traffic assignment model. Based on air quality observations from monitoring stations and a spatial interpolation model, we estimate the outdoor PM2.5 concentrations at a 500-m grid level and map them to road networks. We then estimate the travel exposure for each road segment by multiplying the PM2.5 concentration and travel time spent on the road. By combining the estimated PM2.5 exposure and housing price harnessed from online housing transaction platforms, we discover that in the winter, Beijing commuters with low wealth level are exposed to 13% more PM2.5 per hour than those with high wealth level when staying at home, but exposed to less PM2.5 by 5% when commuting the same distance (due to lighter traffic congestion in suburban areas). We also find that the residents from the southern suburbs of Beijing have both lower level of wealth and higher stay- and travel- exposure to PM2.5, especially in the winter. These findings inform more equitable environmental mitigation policies for future sustainable development in Beijing. Finally, or the first time in the literature, we compare the results of exposure estimated from passive data with subjective measures of perceived air quality (PAQ) from a survey. The PAQ data was collected via a mobile-app. The comparison confirms consistencies in results and the advantages of the big data for air pollution exposure assessments.
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Introduction Previous research suggested that an out-of-hospital cardiac arrest (OHCA) may be triggered by an exposure to ambient pollutants. Objective We investigated the link between OHCA and a short-term exposure to particulate matter (PM) and other pollutants, within extreme climate conditions in Israel and high PM. Methods In a case-crossover analysis, we analyzed all adult cases of OHCA in Israel during 2016–2017. The air-pollution and meteorology data were retrieved from the 132 monitoring stations. All associations at study were investigated using a lag-distributed regression and adjusted to temperature and humidity. Results There were 12401 OHCA cases. Patients experiencing OHCA were likely to be exposed to elevated levels of pollutants, specifically, nitrogen dioxide (NO2) and particulate matter of size ≤2.5 μm (PM2.5) several hours prior to an event, although both at borderline significance, i.e. odds ratio (OR) = 1.20 (95%CI 0.96; 1.51) and OR = 1.15 (95%CI 0.84; 1.60), respectively. An exposure to NO2 was independently associated with OHCA among males (OR = 1.39, 95%CI 0.96; 2.01) and if occurred during the midweek (OR = 1.43, 95%CI 1.03; 1.97). The adverse effect of PM10 was more evident during a weekend (OR = 2.36, 95%CI 0.88; 6.28), as opposed to working days (OR = 0.81, 95%CI 0.45; 1.44). Analysis stratified by regions suggested a spatial variability in pollution associated with OHCA. Conclusions Short-term exposure to high levels of pollution is adversely associated with OHCA independently of meteorological conditions. The magnitude of the effect is modified by patients' demography. Main finding Short-term exposure to high levels of pollution is adversely associated with OHCA. This effect is independent of temperature and humidity.
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