Exposure to Short- and Medium-Chain Chlorinated Paraffins and the Risk of Gestational Diabetes Mellitus: A Nested Case-Control Study in Eastern China

Toxicological studies have indicated that exposure to chlorinated paraffins (CPs) may disrupt intracellular glucose and energy metabolism. However, limited information exists regarding the impact of human CP exposure on glucose homeostasis and its potential association with an increased risk of developing gestational diabetes mellitus (GDM). Here, we conducted a prospective study with a nested case-control design to evaluate the link between short- and medium-chain CP (SCCPs and MCCPs) exposures during pregnancy and the risk of GDM. Serum samples from 102 GDM-diagnosed pregnant women and 204 healthy controls were collected in Hangzhou, Eastern China. The median (interquartile range, IQR) concentration of SCCPs was 161 (127, 236) ng/mL in the GDM group compared to 127 (96.9, 176) ng/mL in the non-GDM group (p < 0.01). For MCCPs, the GDM group had a median concentration of 144 (117, 174) ng/mL, while the control group was 114 (78.1, 162) ng/mL (p < 0.01). Compared to the lowest quartile as the reference, the adjusted odds ratios (ORs) of GDM were 7.07 (95% CI: 2.87, 17.40) and 3.34 (95% CI: 1.48, 7.53) in the highest quartile of ∑SCCP and ∑MCCP levels, respectively, with MCCPs demonstrating an inverted U-shaped association with GDM. Weighted quantile sum regression evaluated the joint effects of all CPs on GDM and glucose homeostasis. Among all CP congeners, C13H23Cl5 and C10H16Cl6 were the crucial variables driving the positive association with the GDM risk. Our results demonstrated a significant positive association between CP concentration in maternal serum and GDM risk, and exposure to SCCPs and MCCPs may disturb maternal glucose homeostasis. These findings contribute to a better understanding of the health risks of CP exposure and the role of environmental contaminants in the pathogenesis of GDM.

Groundwater Salinity Across India: Predicting Occurrences and Controls by Field-Observations and Machine Learning Modeling

Elevated groundwater salinity is unsuitable for drinking and harmful to crop production. Thus, it is crucial to determine groundwater salinity distribution, especially where drinking and agricultural water requirements are largely supported by groundwater. This study used field observation (n = 20,994)-based machine learning models to determine the probabilistic distribution of elevated groundwater salinity (electrical conductivity as a proxy, >2000 μS/cm) at 1 km2 across parts of India for near groundwater-table conditions. The final predictions were made by using the best-performing random forest model. The validation performance also demonstrated the robustness of the model (with 77% accuracy). About 29% of the study area (including 25% of entire cropland areas) was estimated to have elevated salinity, dominantly in northwestern and peninsular India. Also, parts of the northwestern and southeastern coasts, adjoining the Arabian Sea and the Bay of Bengal, were assessed with elevated salinity. The climate was delineated as the dominant factor influencing groundwater salinity occurrence, followed by distance from the coast, geology (lithology), and depth of groundwater. Consequently, ∼330 million people, including ∼109 million coastal populations, were estimated to be potentially exposed to elevated groundwater salinity through groundwater-sourced drinking water, thus substantially limiting clean water access.

Comparison of external dose estimates using different retrospective dosimetry methods in the settlements located near Semipalatinsk Nuclear Test Site, Republic of Kazakhstan

For correct assessment of health risks after low-dose irradiation, calculation of radiation exposure estimates is crucial. To verify the calculated absorbed doses, instrumental methods of retrospective dosimetry are used. We compared calculated and instrumental-based estimates of external absorbed doses in the residents of Dolon, Mostik and Cheremushki villages, Kazakhstan, affected by the first nuclear weapon test performed at the Semipalatinsk Nuclear Test Site (SNTS) on August 29, 1949. The 'instrumental' doses were retrospectively estimated using the Luminescence Retrospective Dosimetry (LRD) and Electron Spin Resonance (ESR) methods. Correlation between the calculated individual cumulative external absorbed whole-body doses based on typical input data and ESR-based individual doses in the same people was strong (r = 0.782). It was even stronger between the calculated doses based on individual questionnaires' input data and the ESR-based doses (r = 0.940). Application of the LRD method is useful for validation of the calculated settlement-average cumulated external absorbed dose to air. Reconstruction of external exposure can be supplemented with the data from later measurements of soil contamination with long-lived radionuclides, such as, 137Cs. Our results show the reliability of the calculational method used for the retrospective assessment of individual external doses.

Five Years of Accurate PM2.5 Measurements Demonstrate the Value of Low-Cost PurpleAir Monitors in Areas Affected by Woodsmoke

Low-cost optical sensors are used in many countries to monitor fine particulate (PM2.5) air pollution, especially in cities and towns with large spatial and temporal variation due to woodsmoke pollution. Previous peer-reviewed research derived calibration equations for PurpleAir (PA) sensors by co-locating PA units at a government regulatory air pollution monitoring site in Armidale, NSW, Australia, a town where woodsmoke is the main source of PM2.5 pollution. The calibrations enabled the PA sensors to provide accurate estimates of PM2.5 that were almost identical to those from the NSW Government reference equipment and allowed the high levels of wintertime PM2.5 pollution and the substantial spatial and temporal variation from wood heaters to be quantified, as well as the estimated costs of premature mortality exceeding $10,000 per wood heater per year. This follow-up study evaluates eight PA sensors co-located at the same government site to check their accuracy over the following four years, using either the original calibrations, the default woodsmoke equation on the PA website for uncalibrated sensors, or the ALT-34 conversion equation (see text). Minimal calibration drift was observed, with year-round correlations, r = 0.98 ± 0.01, and root mean square error (RMSE) = 2.0 μg/m3 for daily average PA PM2.5 vs. reference equipment. The utitilty of the PA sensors without prior calibration at locations affected by woodsmoke was also demonstrated by the year-round correlations of 0.94 and low RMSE between PA (woodsmoke and ALT-34 conversions) and reference PM2.5 at the NSW Government monitoring sites in Orange and Gunnedah. To ensure the reliability of the PA data, basic quality checks are recommended, including the agreement of the two laser sensors in each PA unit and removing any transient spikes affecting only one sensor. In Armidale, from 2019 to 2022, the continuing high spatial variation in the PM2.5 levels observed during the colder months was many times higher than any discrepancies between the PA and reference measurements. Particularly unhealthy PM2.5 levels were noted in southern and eastern central Armidale. The measurements inside two older weatherboard houses in Armidale showed that high outdoor pollution resulted in high pollution inside the houses within 1-2 h. Daily average PM2.5 concentrations available on the PA website allow air pollution at different sites across regions (and countries) to be compared. Such comparisons revealed major elevations in PA PM2.5 at Gunnedah, Orange, Monash (Australian Capital Territory), and Christchurch (New Zealand) during the wood heating season. The data for Gunnedah and Muswellbrook suggest a slight underestimation of PM2.5 at other times of the year when there are proportionately more dust and other larger particles. A network of appropriately calibrated PA sensors can provide valuable information on the spatial and temporal variation in the air pollution that can be used to identify pollution hotspots, improve estimates of population exposure and health costs, and inform public policy.

Population Exposure Changes to Mean and Extreme Climate Events Over Pakistan and Associated Mechanisms

The increasing prevalence of warmer trends and climate extremes exacerbate the population's exposure to urban settlements. This work investigated population exposure changes to mean and extreme climate events in different Agro-Ecological Zones (AEZs) of Pakistan and associated mechanisms (1979-2020). Spatiotemporal trends in mean and extreme temperatures revealed significant warming mainly over northern, northeastern, and southern AEZs. In contrast, mean-to-extreme precipitation changes showed non-uniform patterns with a significant increase in the northeast AEZs. Population exposure to mean (extreme) temperature and precipitation events increased two-fold during 2000-2020. The AEZs in urban settlements (i.e., Indus Delta, Northern Irrigated Plain, and Barani/Rainfall) show a maximum exposure to extreme temperatures of about 70-100 × 106 (person-days) in the reference period (1979-1999), which increases to 140-200 × 106 person-days in the recent period (2000-2020). In addition, the highest exposure to extreme precipitation days also increases to 40-200 × 106 person-days during 2000-2020 than 1979-1999 (20-100 × 106) person-days. Relative changes in exposure are large (60%-90%) for the AEZs across northeast Pakistan, justifying the spatial population patterns over these zones. Overall, the observed changes in exposure are primarily attributed to the climate effect (50%) over most AEZs except Northern Irrigated Plain for R10 and R20 events, where the interaction effect takes the lead. The population exposure rapidly increased over major AEZs of Pakistan, which could be more vulnerable to extreme events due to rapid urbanization and population growth in the near future.

Advances in Simulating the Global Spatial Heterogeneity of Air Quality and Source Sector Contributions: Insights into the Global South

High-resolution simulations are essential to resolve fine-scale air pollution patterns due to localized emissions, nonlinear chemical feedbacks, and complex meteorology. However, high-resolution global simulations of air quality remain rare, especially of the Global South. Here, we exploit recent developments to the GEOS-Chem model in its high-performance implementation to conduct 1-year simulations in 2015 at cubed-sphere C360 (∼25 km) and C48 (∼200 km) resolutions. We investigate the resolution dependence of population exposure and sectoral contributions to surface fine particulate matter (PM2.5) and nitrogen dioxide (NO2), focusing on understudied regions. Our results indicate pronounced spatial heterogeneity at high resolution (C360) with large global population-weighted normalized root-mean-square difference (PW-NRMSD) across resolutions for primary (62-126%) and secondary (26-35%) PM2.5 species. Developing regions are more sensitive to spatial resolution resulting from sparse pollution hotspots, with PW-NRMSD for PM2.5 in the Global South (33%), 1.3 times higher than globally. The PW-NRMSD for PM2.5 for discrete southern cities (49%) is substantially higher than for more clustered northern cities (28%). We find that the relative order of sectoral contributions to population exposure depends on simulation resolution, with implications for location-specific air pollution control strategies.

Exposure Assessment of Ambient PM2.5 Levels during a Sequence of Dust Episodes: A Case Study Coupling the WRF-Chem Model with GIS-Based Postprocessing

A sequence of dust intrusions occurred from the Sahara Desert to the central Mediterranean in the second half of June 2021. This event was simulated by means of the Weather Research and Forecasting coupled with chemistry (WRF-Chem) regional chemical transport model (CTM). The population exposure to the dust surface PM2.5 was evaluated with the open-source quantum geographical information system (QGIS) by combining the output of the CTM with the resident population map of Italy. WRF-Chem analyses were compared with spaceborne aerosol observations derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) and, for the PM2.5 surface dust concentration, with the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) reanalysis. Considering the full-period (17-24 June) and area-averaged statistics, the WRF-Chem simulations showed a general underestimation for both the aerosol optical depth (AOD) and the PM2.5 surface dust concentration. The comparison of exposure classes calculated for Italy and its macro-regions showed that the dust sequence exposure varies with the location and entity of the resident population amount. The lowest exposure class (up to 5 µg m^-3) had the highest percentage (38%) of the population of Italy and most of the population of north Italy, whereas more than a half of the population of central, south and insular Italy had been exposed to dust PM2.5 in the range of 15-25 µg m^-3. The coupling of the WRF-Chem model with QGIS is a promising tool for the management of risks posed by extreme pollution and/or severe meteorological events. Specifically, the present methodology can also be applied for operational dust forecasting purposes, to deliver safety alarm messages to areas with the most exposed population.

Global Population Exposure to Extreme Temperatures and Disease Burden

The frequency and duration of extreme temperature events continues to increase worldwide. However, the scale of population exposure and its quantitative relationship with health risks remains unknown on a global scale, limiting our ability to identify policy priorities in response to climate change. Based on data from 171 countries between 2010 and 2019, this study estimated the exposure of vulnerable populations to extreme temperatures, and their contemporary and lag associations with disease burden attributed to non-optimal temperatures. Fixed-effects models and dynamic panel models were applied. Increased vulnerable population exposure to extreme temperatures had adverse contemporary effects on the burden of disease attributed to non-optimal temperature. Health risks stemming from extreme cold could accumulate to a greater extent, exhibiting a larger lag effect. Population exposure to extreme cold was mainly distributed in high-income countries, while extreme heat occurred more in low-income and middle-income countries. However, the association between population exposure to extreme cold and burden of disease was much stronger in low-income and middle-income countries than in high-income countries, whereas the effect size of population exposure to extreme heat was similar. Our study highlighted that differential strategies should be determined and implemented according to the characteristics in different countries.

Evaluation and Projection of Surface PM2.5 and Its Exposure on Population in Asia Based on the CMIP6 GCMs

This paper evaluates the historical simulated surface concentrations of particulate matter small than 2.5 µm in diameter (PM_2.5) and its components (black carbon (BC), dust, SO₄, and organic aerosol (OA)) in Asia, which come from Coupled Model Intercomparison Project Phase 6 (CMIP6). In addition, future projected changes of surface PM_2.5 and its components, as well as their exposure to population, under the different Shared Socioeconomic Pathway (SSP) scenarios are also provided. Results show that the simulated spatial distribution of surface PM_2.5 concentrations is consistent with the Modern-Era Retrospective Analysis for Research and Applications version 2 (MERRA-2) and Socioeconomic Data and Applications Center (SEDAC). The model spreads are small/large over the regions with low/high climatic mean surface PM_2.5 concentrations, i.e., Northern Asia/Saudi Arabia, Iran, and Xinjiang Province of China. The multi-model ensemble of CMIP6 reproduces the main features of annual cycles and seasonal variations in Asia and its sub-regions. Under the scenarios of SSP1-2.6, SSP2-4.5, and SSP5-8.5, compared to the present-day period of 1995-2014, annual mean surface PM_2.5 concentrations are projected to decrease in Asia, with obvious differences among the scenarios. Meanwhile, the magnitudes and timings of changes at the regional scale are quite different, with the largest decreases in South Asia (SAS). Under SSP3-7.0, the increase of surface PM_2.5 concentrations in SAS is the largest, with the increase value of 8 μg/m³ in 2050; while under SSP370-lowNTCF, which assumes stronger levels of air quality control measures relative to the SSP3-7.0, the decreases of surface PM_2.5 concentrations in SAS, East Asia (EAS) and Southeast Asia (SEAS) are the largest. The characteristics of seasonal trends are consistent with that of the annual trend. The trends in the concentrations of surface PM_2.5 and its components are similar. The population-weighted average values of surface PM_2.5 concentrations are projected to decrease in Central Asia (CAS), EAS, North Asia (NAS), and SEAS, and it indicates that the surface PM_2.5 concentrations over the most populated area of Asia will decrease. In SAS, because of its large population, the impact of air pollutants on human health is still disastrous in the future. In summary, the surface PM_2.5 concentrations over the most area of Asia will decrease, which is beneficial to air quality and human health; under SSP370-lowNTCF, the reduction of short-lived climate forcers (SLCFs) will further improve air quality.

Spatio-Temporal Variation-Induced Group Disparity of Intra-Urban NO2 Exposure

Previous studies on exposure disparity have focused more on spatial variation but ignored the temporal variation of air pollution; thus, it is necessary to explore group disparity in terms of spatio-temporal variation to assist policy-making regarding public health. This study employed the dynamic land use regression (LUR) model and mobile phone signal data to illustrate the variation features of group disparity in Shanghai. The results showed that NO₂ exposure followed a bimodal, diurnal variation pattern and remained at a high level on weekdays but decreased on weekends. The most critical at-risk areas were within the central city in areas with a high population density. Moreover, women and the elderly proved to be more exposed to NO₂ pollution in Shanghai. Furthermore, the results of this study showed that it is vital to focus on land-use planning, transportation improvement programs, and population agglomeration to attenuate exposure inequality.

Multi-Model Ensemble Projections of Winter Extreme Temperature Events on the Chinese Mainland

Based on the downscaling data of multi-model ensembles of 26 global climate models (GCMs) from the Coupled Model Intercomparison Project Phase 6, this study calculated the extreme climate indices defined by the Expert Team on Climate Change Detection and Indices and the warm winter extreme grade indices to explore winter climate response on the Chinese mainland under different shared socioeconomic pathways (SSPs) and representative concentration pathways. The results showed that the temperature in winter increased overall, with the highest temperature increases of 0.31 °C/10a (Celsius per decade) (SSP245) and 0.51 °C/10a (SSP585) and the lowest temperature increases of 0.30 °C/10a (SSP245) and 0.49 °C/10a (SSP585). Warm-related extreme weather events such as warm days and warm spell duration indices showed an increasing trend, whereas cold-related extreme weather events such as cold spell duration indices, cold nights, ice days, and frost days showed a decreasing trend. On the regional scale, the maximum temperature increased by more than 2 °C/10a (SSP245) and 0.4 °C/10a (SSP585), except in South China, and the minimum temperature increased faster in Qinghai-Tibet and Northeast China compared to elsewhere on the Chinese mainland. Compared with that under SSP585, the frequency and intensity of warm winters in the latter half of the 21st century were lower under SSP245. At the end of the 21st century, under the SSP245 scenario, warm winter frequency in most regions will be reduced to below 60%, but under the SSP585 scenario, it will be more than 80%. Population exposures all showed a downward trend, mainly due to the reduction of warm winter events and the decline of the population under the SSP245 and SSP585 scenarios, respectively. If the greenhouse gas emission path is controlled in the SSP245 scenario, the population exposure risk in warm winters can be decreased by 25.87%. This study observed a consistent warming trend on the Chinese mainland under all SSPs in the 21st century and found that stricter emission reduction policies can effectively decrease the population exposure to warm winters.

[Estimation of Surface Ozone Concentration and Health Impact Assessment in China]

Within the context of PM_2.5 concentrations decreasing annually, ozone concentrations have increased instead of decreased, and ozone has become one of the main pollutants in the warm season in China. Based on the idea of big data association analysis, the extreme gradient boosting (XGBoost) ozone concentration estimation model was constructed and developed to estimate the maximum daily 8 h average ozone concentration (O₃_8h) in China in 2019 for human exposure assessment. The model input ground monitoring station data, high-resolution remote-sensing satellite data, meteorological data, emission inventory data, digital elevation model (DEM) data, and population data were used to capture the temporal and spatial variation of O₃_8h. In this study, ten-fold cross-validation was used to evaluate the estimation performance of the model (R²=0.871, RMSE=11.7 μg·m^-3). Compared to those with the random forest (RF) model and kernel ridge regression (KRR) model, due to the improvement in the algorithm itself and the advancement of parallel processing, the estimation results of the XGBoost model showed higher accuracy (RF:R²=0.864, RMSE=12.387 μg·m^-3). The KRR model was as follows:R²=0.582, RMSE=23.1 μg·m^-3, and the computational efficiency of the model was significantly improved. At the same time, the level of ozone exposure and the relative risk of death due to chronic obstructive pulmonary disease (COPD) in China's provinces and cities were evaluated. The results showed that the top five number of days exceeding the standard occurred in Shandong Province, Henan Province, Hebei Province, Anhui Province, and the Ningxia Hui Autonomous Region. In terms of exposure intensity, Hebei Province, Shandong Province, Shanxi Province, Tianjin City, and Jiangsu Province ranked the top five in terms of population weighted ozone concentration. In terms of health effects, the relative risk of COPD death showed seasonal changes, with the highest in summer and the lowest in winter.

A Health Impact and Economic Loss Assessment of O3 and PM2.5 Exposure in China From 2015 to 2020

China is in a critical air quality management stage. Rapid industrial development and urbanization has resulted in non-ignorable air pollution, which seriously endangers human health. Assessment of the health impacts and economic losses of air pollution is essential for the prevention and control policy formulation. Based on ozone (O₃) and fine particulate matter concentration (PM_2.5) monitoring data in 331 Chinese cities from 2015 to 2020, this study evaluated the health effects and the corresponding economic losses of O₃ and PM_2.5 pollution on three health endpoints. The ratio of population exposed to O₃ levels that exceeded the Chinese Ambient Air Quality Standards (CAAQS) increased from 13.35% in 2015 to 14.15% in 2020, which resulted in 133,415 (2015) - 156,173 (2020) all-cause deaths, 88,941 (2015) - 104,051 (2020) cardiovascular deaths, and 28,614 (2015) - 33,456 (2020) respiratory deaths. The ratio of population exposed to PM_2.5 levels that exceeded the CAAQS decreased, but in many regions, especially in North China and the Yangtze River Delta, the PM_2.5 concentration remained high. By 2020, nearly half of the population in China was still exposed to PM_2.5 levels that exceeded the CAAQS, and the corresponding economic losses reached CNY 3.46 and 3.05 billion, respectively. These results improved the understanding of the spatial-temporal variation trends of major air pollutants at city scale in China, and emphasize the continued coordination urgently needed for controlling O₃ and PM_2.5 following the implementation of the 2013 policy to mitigate air pollution to protect human health.

Substantial Reduction in Particulate Matter Air Pollution across Europe during 2006-2019: A Spatiotemporal Modeling Analysis

Air pollution poses the largest environmental health risk in Europe. Particulate matter (PM) concentrations are the most harmful pollutants representing the main air quality indicator in the Sustainable Development Goals (SDGs). The air quality surveillance in Europe is based on a monitoring network that is too coarse for a comprehensive evaluation of the air pollution burden. We link raw pollutant data with remotely sensed products using Bayesian geostatistical models and for the first time estimate pan-European near-surface concentrations of both fine (PM2.5) and coarse (PM10) particles at 1 km2 spatial resolution during 2006-2019. We evaluate the compliance with the air quality thresholds set by the World Health Organization (WHO) and the European Union (EU) and assess country-wise trends. The results show that during the last 14 years, PM10 and PM2.5 concentrations declined by 36.5% (95% credible interval: 30.3, 41.9%) and 39.1% (26.6, 50.5%), respectively. The number of people exposed to PM10 levels above the WHO thresholds decreased from 78.3% (52.6, 91.8%) in 2006 to 28.4% (16.2, 43.7%) in 2019; for PM2.5, the decrease was smaller: from 91.0% (61.3, 99.1%) exposed in 2006 to 53.6% (33.5, 76.3%) in 2019. Although there is a clear improvement in the overall picture, stricter measures are needed to ensure compliance with the WHO guidelines.

Estimating the Acute Health Impacts of Fire-Originated PM2.5 Exposure During the 2017 California Wildfires: Sensitivity to Choices of Inputs

Exposure to wildfire smoke increases the risk of respiratory and cardiovascular hospital admissions. Health impact assessments, used to inform decision-making processes, characterize the health impacts of environmental exposures by combining preexisting epidemiological concentration-response functions (CRFs) with estimates of exposure. These two key inputs influence the magnitude and uncertainty of the health impacts estimated, but for wildfire-related impact assessments the extent of their impact is largely unknown. We first estimated the number of respiratory, cardiovascular, and asthma hospital admissions attributable to fire-originated PM2.5 exposure in central California during the October 2017 wildfires, using Monte Carlo simulations to quantify uncertainty with respect to the exposure and epidemiological inputs. We next conducted sensitivity analyses, comparing four estimates of fire-originated PM2.5 and two CRFs, wildfire and nonwildfire specific, to understand their impact on the estimation of excess admissions and sources of uncertainty. We estimate the fires accounted for an excess 240 (95% CI: 114, 404) respiratory, 68 (95% CI: -10, 159) cardiovascular, and 45 (95% CI: 18, 81) asthma hospital admissions, with 56% of admissions occurring in the Bay Area. Although differences between impact assessment methods are not statistically significant, the admissions estimates' magnitude is particularly sensitive to the CRF specified while the uncertainty is most sensitive to estimates of fire-originated PM2.5. Not accounting for the exposure surface's uncertainty leads to an underestimation of the uncertainty of the health impacts estimated. Employing context-specific CRFs and using accurate exposure estimates that combine multiple data sets generates more certain estimates of the acute health impacts of wildfires.

Health Impact of Air Pollution from Shipping in the Baltic Sea: Effects of Different Spatial Resolutions in Sweden

In 2015, stricter regulations to reduce sulfur dioxide emissions and particulate air pollution from shipping were implemented in the Baltic Sea. We investigated the effects on population exposure to particles <2.5 µm (PM_2.5) from shipping and estimated related morbidity and mortality in Sweden’s 21 counties at different spatial resolutions. We used a regional model to estimate exposure in Sweden and a city-scale model for Gothenburg. Effects of PM_2.5 exposure on total mortality, ischemic heart disease, and stroke were estimated using exposure–response functions from the literature and combining them into disability-adjusted life years (DALYS). PM_2.5 exposure from shipping in Gothenburg decreased by 7% (1.6 to 1.5 µg/m³) using the city-scale model, and 35% (0.5 to 0.3 µg/m³) using the regional model. Different population resolutions had no effects on population exposures. In the city-scale model, annual premature deaths due to shipping PM_2.5 dropped from 97 with the high-sulfur scenario to 90 in the low-sulfur scenario, and in the regional model from 32 to 21. In Sweden, DALYs lost due to PM_2.5 from Baltic Sea shipping decreased from approximately 5700 to 4200. In conclusion, sulfur emission restrictions for shipping had positive effects on health, but the model resolution affects estimations.

Environmental Health Surveillance System for a Population Using Advanced Exposure Assessment

Human exposure to air pollution is a major public health concern. Environmental policymakers have been implementing various strategies to reduce exposure, including the 10th-day-no-driving system. To assess exposure of an entire population of a community in a highly polluted area, pollutant concentrations in microenvironments and population time–activity patterns are required. To date, population exposure to air pollutants has been assessed using air monitoring data from fixed atmospheric monitoring stations, atmospheric dispersion modeling, or spatial interpolation techniques for pollutant concentrations. This is coupled with census data, administrative registers, and data on the patterns of the time-based activities at the individual scale. Recent technologies such as sensors, the Internet of Things (IoT), communications technology, and artificial intelligence enable the accurate evaluation of air pollution exposure for a population in an environmental health context. In this study, the latest trends in published papers on the assessment of population exposure to air pollution were reviewed. Subsequently, this study proposes a methodology that will enable policymakers to develop an environmental health surveillance system that evaluates the distribution of air pollution exposure for a population within a target area and establish countermeasures based on advanced exposure assessment.

Integrating Modes of Transport in a Dynamic Modelling Approach to Evaluate Population Exposure to Ambient NO2 and PM2.5 Pollution in Urban Areas

To evaluate the effectiveness of alternative policies and measures to reduce air pollution effects on urban citizen's health, population exposure assessments are needed. Due to road traffic emissions being a major source of emissions and exposure in European cities, it is necessary to account for differentiated transport environments in population dynamics for exposure studies. In this study, we applied a modelling system to evaluate population exposure in the urban area of Hamburg in 2016. The modeling system consists of an urban-scale chemistry transport model to account for ambient air pollutant concentrations and a dynamic time-microenvironment-activity (TMA) approach, which accounts for population dynamics in different environments as well as for infiltration of outdoor to indoor air pollution. We integrated different modes of transport in the TMA approach to improve population exposure assessments in transport environments. The newly developed approach reports 12% more total exposure to NO₂ and 19% more to PM_2.5 compared with exposure estimates based on residential addresses. During the time people spend in different transport environments, the in-car environment contributes with 40% and 33% to the annual sum of exposure to NO₂ and PM_2.5, in the walking environment with 26% and 30%, in the cycling environment with 15% and 17% and other environments (buses, subway, suburban, and regional trains) with less than 10% respectively. The relative contribution of road traffic emissions to population exposure is highest in the in-car environment (57% for NO₂ and 15% for PM_2.5). Results for population-weighted exposure revealed exposure to PM_2.5 concentrations above the WHO AQG limit value in the cycling environment. Uncertainties for the exposure contributions arising from emissions and infiltration from outdoor to indoor pollutant concentrations range from -12% to +7% for NO₂ and PM_2.5. The developed "dynamic transport approach" is integrated in a computationally efficient exposure model, which is generally applicable in European urban areas. The presented methodology is promoted for use in urban mobility planning, e.g., to investigate on policy-driven changes in modal split and their combined effect on emissions, population activity and population exposure.

Quantifying the Health Burden Misclassification from the Use of Different PM2.5 Exposure Tier Models: A Case Study of London

Exposure to PM_2.5 has been associated with increased mortality in urban areas. Hence, reducing the uncertainty in human exposure assessments is essential for more accurate health burden estimates. Here, we quantified the misclassification that occurred when using different exposure approaches to predict the mortality burden of a population using London as a case study. We developed a framework for quantifying the misclassification of the total mortality burden attributable to exposure to fine particulate matter (PM_2.5) in four major microenvironments (MEs) (dwellings, aboveground transportation, London Underground (LU) and outdoors) in the Greater London Area (GLA), in 2017. We demonstrated that differences exist between five different exposure Tier-models with incrementally increasing complexity, moving from static to more dynamic approaches. BenMap-CE, the open source software developed by the U.S. Environmental Protection Agency, was used as a tool to achieve spatial distribution of the ambient concentration by interpolating the monitoring data to the unmonitored areas and ultimately estimating the change in mortality on a fine resolution. Indoor exposure to PM_2.5 is the largest contributor to total population exposure concentration, accounting for 83% of total predicted population exposure, followed by the London Underground, which contributes approximately 15%, despite the average time spent there by Londoners being only 0.4%. After incorporating housing stock and time-activity data, moving from static to most dynamic metric, Inner London showed the highest reduction in exposure concentration (i.e., approximately 37%) and as a result the largest change in mortality (i.e., health burden/mortality misclassification) was observed in central GLA. Overall, our findings showed that using outdoor concentration as a surrogate for total population exposure but ignoring different exposure concentration that occur indoors and time spent in transit, led to a misclassification of 1174–1541 mean predicted mortalities in GLA. We generally confirm that increasing the complexity and incorporating important microenvironments, such as the highly polluted LU, could significantly reduce the misclassification of health burden assessments.

Modeling of the Concentrations of Ultrafine Particles in the Plumes of Ships in the Vicinity of Major Harbors

Marine traffic in harbors can be responsible for significant atmospheric concentrations of ultrafine particles (UFPs), which have widely recognized negative effects on human health. It is therefore essential to model and measure the time evolution of the number size distributions and chemical composition of UFPs in ship exhaust to assess the resulting exposure in the vicinity of shipping routes. In this study, a sequential modelling chain was developed and applied, in combination with the data measured and collected in major harbor areas in the cities of Helsinki and Turku in Finland, during winter and summer in 2010–2011. The models described ship emissions, atmospheric dispersion, and aerosol dynamics, complemented with a time–microenvironment–activity model to estimate the short-term UFP exposure. We estimated the dilution ratio during the initial fast expansion of the exhaust plume to be approximately equal to eight. This dispersion regime resulted in a fully formed nucleation mode (denoted as Nuc₂). Different selected modelling assumptions about the chemical composition of Nuc₂ did not have an effect on the formation of nucleation mode particles. Aerosol model simulations of the dispersing ship plume also revealed a partially formed nucleation mode (Nuc₁; peaking at 1.5 nm), consisting of freshly nucleated sulfate particles and condensed organics that were produced within the first few seconds. However, subsequent growth of the new particles was limited, due to efficient scavenging by the larger particles originating from the ship exhaust. The transport of UFPs downwind of the ship track increased the hourly mean UFP concentrations in the neighboring residential areas by a factor of two or more up to a distance of 3600 m, compared with the corresponding UFP concentrations in the urban background. The substantially increased UFP concentrations due to ship traffic significantly affected the daily mean exposures in residential areas located in the vicinity of the harbors.

Improved Estimates of Population Exposure in Low-Elevation Coastal Zones of China

With sea level predicted to rise and the frequency and intensity of coastal flooding expected to increase due to climate change, high-resolution gridded population datasets have been extensively used to estimate the size of vulnerable populations in low-elevation coastal zones (LECZ). China is the most populous country, and populations in its LECZ grew rapidly due to urbanization and remarkable economic growth in coastal areas. In assessing the potential impacts of coastal hazards, the spatial distribution of population exposure in China’s LECZ should be examined. In this study, we propose a combination of multisource remote sensing images, point-of-interest data, and machine learning methods to improve the performance of population disaggregation in coastal China. The resulting population grid map of coastal China for the reference year 2010, with a spatial resolution of 100 × 100 m, is presented and validated. Then, we analyze the distribution of population in LECZ by overlaying the new gridded population data and LECZ footprints. Results showed that the total population exposed in China’s LECZ in 2010 was 158.2 million (random forest prediction) and 160.6 million (Cubist prediction), which account for 12.17% and 12.36% of the national population, respectively. This study also showed the considerable potential in combining geospatial big data for high-resolution population estimation.

Residential Wood Combustion in Finland: PM2.5 Emissions and Health Impacts with and without Abatement Measures

Exposure to fine particles in ambient air has been estimated to be one of the leading environmental health risks in Finland. Residential wood combustion is the largest domestic source of fine particles, and there is increasing political interest in finding feasible measures to reduce those emissions. In this paper, we present the PM_2.5 emissions from residential wood combustion in Finland, as well as the resulting concentrations. We used population-weighed concentrations in a 250 x 250 m grid as population exposure estimates, with which we calculated the disease burden of the emissions. Compared to a projected baseline scenario, we studied the effect of chosen reduction measures in several abatement scenarios. In 2015, the resulting annual average concentrations were between 0.5 and 2 µg/m³ in the proximity of most cities, and disease burden attributable to residential wood combustion was estimated to be 3400 disability-adjusted life years (DALY) and 200 deaths. Disease burden decreased by 8% in the 2030 baseline scenario and by an additional 63% in the maximum feasible reduction scenario. Informational campaigns and improvement of the sauna stove stock were assessed to be the most feasible abatement measures to be implemented in national air quality policies.

Population dynamics based on mobile phone data to improve air pollution exposure assessments

Air pollution is one of the greatest challenges cities are facing today and improving air quality is a pressing need to reduce negative health impacts. In order to efficiently evaluate which are the most appropriate policies to reduce the impact of urban pollution sources (such as road traffic), it is essential to conduct rigorous population exposure assessments. One of the main limitations associated with those studies is the lack of information about population distribution in the city along the day (population dynamics). The pervasive use of mobile devices in our daily lives opens new opportunities to gather large amounts of anonymized and passively collected geolocation data allowing the analysis of population activity and mobility patterns. This study presents a novel methodology to estimate population dynamics from mobile phone data based on a user-centric mobility model approach. The methodology was tested in the city of Madrid (Spain) to evaluate population exposure to NO₂. A comparison with traditional census-based methods shows relevant discrepancies at disaggregated levels and highlights the need to incorporate mobility patterns into population exposure assessments.

Population Exposure to Ambient PM2.5 at the Subdistrict Level in China

Fine-particulate pollution is a major public health concern in China. Accurate assessment of the population exposed to PM_2.5 requires high-resolution pollution and population information. This paper assesses China’s potential population exposure to PM_2.5, maps its spatiotemporal variability, and simulates the effects of the recent air pollution control policy. We relate satellite-based Aerosol Optical Depth (AOD) retrievals to ground-based PM_2.5 observations. We employ block cokriging (BCK) to improve the spatial interpolation of PM_2.5 distribution. We use the subdistrict level population data to estimate and map the potential population exposure to PM_2.5 pollution in China at the subdistrict level, the smallest administrative unit with public demographic information. During 8 April 2013 and 7 April 2014, China’s population-weighted annual average PM_2.5 concentration was nearly 7 times the annual average level suggested by the World Health Organization (WHO). About 1322 million people, or 98.6% of the total population, were exposed to PM_2.5 at levels above WHO’s daily guideline for longer than half a year. If China can achieve its Action Plan on Prevention and Control of Air Pollution targets by 2017, the population exposed to PM_2.5 above China’s daily standard for longer than half a year will be reduced by 85%.

Quantitative Assessment of Relationship between Population Exposure to PM2.5 and Socio-Economic Factors at Multiple Spatial Scales over Mainland China

Analyzing the association between fine particulate matter (PM_2.5) pollution and socio-economic factors has become a major concern in public health. Since traditional analysis methods (such as correlation analysis and geographically weighted regression) cannot provide a full assessment of this relationship, the quantile regression method was applied to overcome such a limitation at different spatial scales in this study. The results indicated that merely 3% of the population and 2% of the Gross Domestic Product (GDP) occurred under an annually mean value of 35 μg/m³ in mainland China, and the highest population exposure to PM_2.5 was located in a lesser-known city named Dazhou in 2014. The analysis results at three spatial scales (grid-level, county-level, and city-level) demonstrated that the grid-level was the optimal spatial scale for analysis of socio-economic effects on exposure due to its tiny uncertainty, and the population exposure to PM_2.5 was positively related to GDP. An apparent upward trend of population exposure to PM_2.5 emerged at the 80th percentile GDP. For a 10 thousand yuan rise in GDP, population exposure to PM_2.5 increases by 1.05 person/km² at the 80th percentile, and 1.88 person/km² at the 95th percentile, respectively.

Rapid Population Growth in Chinese Floodplains from 1990 to 2015

Although China suffers from frequent and disastrous floods, the spatiotemporal pattern of its population living in the floodplain (PopF) is still unknown. This strongly limits our understanding of flood risk and the effectiveness of mitigation efforts. Here we present the first quantification of Chinese PopF and its dynamics, based on newly-available population datasets for years 1990, 2000, 2010, and 2015 and on a flood map. We found that the PopF in 2015 was 453.3 million and accounted for 33.0% of the total population, with a population density 3.6 times higher than outside floodplains. From 1990 to 2015, the PopF increased by 1.3% annually, overwhelmingly faster than elsewhere (0.5%). A rising proportion (from 53.2% in 1990 to 55.6% in 2015) of the PopF resided in flood zones deeper than 2 m. Moreover, the PopF is expected to increase rapidly in the coming decades. We also found the effect of flood memory on controlling PopF growth and its decay over time. These findings imply an exacerbating flood risk in China, which is concerning in the light of climate change and rapid socioeconomic development.

Air pollution as a risk factor in health impact assessments of a travel mode shift towards cycling

BACKGROUND

Promotion of active commuting provides substantial health and environmental benefits by influencing air pollution, physical activity, accidents, and noise. However, studies evaluating intervention and policies on a mode shift from motorized transport to cycling have estimated health impacts with varying validity and precision.

OBJECTIVE

To review and discuss the estimation of air pollution exposure and its impacts in health impact assessment studies of a shift in transport from cars to bicycles in order to guide future assessments.

METHODS

A systematic database search of PubMed was done primarily for articles published from January 2000 to May 2016 according to PRISMA guidelines.

RESULTS

We identified 18 studies of health impact assessment of change in transport mode. Most studies investigated future hypothetical scenarios of increased cycling. The impact on the general population was estimated using a comparative risk assessment approach in the majority of these studies, whereas some used previously published cost estimates. Air pollution exposure during cycling was estimated based on the ventilation rate, the pollutant concentration, and the trip duration. Most studies employed exposure-response functions from studies comparing background levels of fine particles between cities to estimate the health impacts of local traffic emissions. The effect of air pollution associated with increased cycling contributed small health benefits for the general population, and also only slightly increased risks associated with fine particle exposure among those who shifted to cycling. However, studies calculating health impacts based on exposure-response functions for ozone, black carbon or nitrogen oxides found larger effects attributed to changes in air pollution exposure.

CONCLUSION

A large discrepancy between studies was observed due to different health impact assessment approaches, different assumptions for calculation of inhaled dose and different selection of dose-response functions. This kind of assessments would improve from more holistic approaches using more specific exposure-response functions.

Spatiotemporal Changes in Fine Particulate Matter Pollution and the Associated Mortality Burden in China between 2015 and 2016

In recent years, research on the spatiotemporal distribution and health effects of fine particulate matter (PM2.5) has been conducted in China. However, the limitations of different research scopes and methods have led to low comparability between regions regarding the mortality burden of PM2.5. A kriging model was used to simulate the distribution of PM2.5 in 2015 and 2016. Relative risk (RR) at a specified PM2.5 exposure concentration was estimated with an integrated exposure-response (IER) model for different causes of mortality: lung cancer (LC), ischaemic heart disease (IHD), cerebrovascular disease (stroke) and chronic obstructive pulmonary disease (COPD). The population attributable fraction (PAF) was adopted to estimate deaths attributed to PM2.5. 72.02% of cities experienced decreases in PM2.5 from 2015 to 2016. Due to the overall decrease in the PM2.5 concentration, the total number of deaths decreased by approximately 10,658 per million in 336 cities, including a decrease of 1400, 1836, 6312 and 1110 caused by LC, IHD, stroke and COPD, respectively. Our results suggest that the overall PM2.5 concentration and PM2.5-related deaths exhibited decreasing trends in China, although air quality in local areas has deteriorated. To improve air pollution control strategies, regional PM2.5 concentrations and trends should be fully considered.

Increase of Elderly Population in the Rainstorm Hazard Areas of China

In light of global warming, increased extreme precipitation events have enlarged the population exposed to floods to some extent. Extreme precipitation risk assessments are of great significance in China and allow for the response to climate change and mitigation of risks to the population. China is one of the countries most influenced by climate change and has unique national population conditions. The influence of extreme precipitation depends on the degree of exposure and vulnerability of the population. Accurate assessments of the population exposed to rising rainstorm trends are crucial to mapping extreme precipitation risks. Studying the population exposed to rainstorm hazard areas (RSHA) at the microscale is extremely urgent, due to the local characteristics of extreme precipitation events and regional diversity of the population. The spatial distribution of population density was mapped based on the national population census data from China in 1990, 2000 and 2010. RSHA were also identified using precipitation data from 1975-2015 in China, and the rainstorm tendency values were mapped using GIS in this paper. The spatial characteristics of the rainstorm tendencies were then analyzed. Finally, changes in the population in the RSHA are discussed. The results show that the extreme precipitation trends are increasing in southeastern China. From 1990 to 2010, the population in RSHA increased by 110 million, at a rate of 14.6%. The elderly in the region increased by 38 million at a rate of 86.4%. Studying the size of the population exposed to rainstorm hazards at the county scale can provide scientific evidence for developing disaster prevention and mitigation strategies from the bottom up.

PM2.5, Population Exposure and Economic Effects in Urban Agglomerations of China Using Ground-Based Monitoring Data

This paper adopts the PM_2.5 concentration data obtained from 1497 station-based monitoring sites, population and gross domestic product (GDP) census data, revealing population exposure and economic effects of PM_2.5 in four typical urban agglomerations of China, i.e., Beijing-Tianjin-Hebei (BTH), the Yangtze River delta (YRD), the Pearl River delta (PRD), and Chengdu-Chongqing (CC). The Cokriging interpolation method was used to estimate the PM_2.5 concentration from station-level to grid-level. Next, an evaluation was conducted mainly at the grid-level with a cell size of 1 × 1 km, assisted by the urban agglomeration scale. Criteria including the population-weighted mean, the cumulative percent distribution and the correlation coefficient were applied in our evaluation. The results showed that the spatial pattern of population exposure in BTH was consistent with that of PM_2.5 concentration, as well as changes in elevation. The topography was also an important factor in the accumulation of PM_2.5 in CC. Moreover, the most polluted urban agglomeration based on the population-weighted mean was BTH, while the least was PRD. In terms of the cumulative percent distribution, only 0.51% of the population who lived in the four urban agglomerations, and 2.33% of the GDP that was produced in the four urban agglomerations, were associated with an annual PM_2.5 concentration smaller than the Chinese National Ambient Air Quality Standard of 35 µg/m³. This indicates that the majority of people live in the high air polluted areas, and economic development contributes to air pollution. Our results are supported by the high correlation between population exposure and the corresponding GDP in each urban agglomeration.

and a Real-Time Web Application

Appropriate spatiotemporal interpolation is critical to the assessment of relationships between environmental exposures and health outcomes. A powerful assessment of human exposure to environmental agents would incorporate spatial and temporal dimensions simultaneously. This paper compares shape function (SF)-based and inverse distance weighting (IDW)-based spatiotemporal interpolation methods on a data set of PM2.5 data in the contiguous U.S. Particle pollution, also known as particulate matter (PM), is composed of microscopic solids or liquid droplets that are so small that they can get deep into the lungs and cause serious health problems. PM2.5 refers to particles with a mean aerodynamic diameter less than or equal to 2.5 micrometers. Based on the error statistics results of k-fold cross validation, the SF-based method performed better overall than the IDW-based method. The interpolation results generated by the SF-based method are combined with population data to estimate the population exposure to PM2.5 in the contiguous U.S. We investigated the seasonal variations, identified areas where annual and daily PM2.5 were above the standards, and calculated the population size in these areas. Finally, a web application is developed to interpolate and visualize in real time the spatiotemporal variation of ambient air pollution across the contiguous U.S. using air pollution data from the U.S. Environmental Protection Agency (EPA)'s AirNow program.

Estimated collective effective dose to the population from radiological examinations in Slovenia

Background

The aim of the study was to systematically evaluate population exposure from diagnostic and interventional radiological procedures in Slovenia.

Methods

The study was conducted in scope of the “Dose Datamed 2” project. A standard methodology based on 20 selected radiological procedures was adopted. Frequencies of the procedures were determined via questionnaires that were sent to all providers of radiological procedures while data about patient exposure per procedure were collected from existing databases. Collective effective dose to the population and effective dose per capita were estimated from the collected data (DLP for CT, MGD for mammography and DAP for other procedures) using dose conversion factors.

Results

The total collective effective dose to the population from radiological in 2011 was estimated to 1300 manSv and an effective dose per capita to 0.6 mSv of which approximately 2/3 are due to CT procedures.

Conclusions

The first systematic study of population exposure to ionising radiation from radiological procedures in Slovenia was performed. The results show that the exposure in Slovenia is under the European average. It confirmed large contributions of computed tomography and interventional procedures, identifying them as the areas that deserve special attention when it comes to justification and optimisation.

Estimated collective effective dose to the population from nuclear medicine examinations in Slovenia

Background

A national survey of patient exposure from nuclear medicine diagnostic procedures was performed by Slovenian Radiation Protection Administration in order to estimate their contribution to the collective effective dose to the population of Slovenia.

Methods

A set of 36 examinations with the highest contributions to the collective effective dose was identified. Data about frequencies and average administered activities of radioisotopes used for those examinations were collected from all nuclear medicine departments in Slovenia. A collective effective dose to the population and an effective dose per capita were estimated from the collected data using dose conversion factors.

Results

The total collective effective dose to the population from nuclear medicine diagnostic procedures in 2011 was estimated to 102 manSv, giving an effective dose per capita of 0.05 mSv.

Conclusions

The comparison of results of this study with studies performed in other countries indicates that the nuclear medicine providers in Slovenia are well aware of the importance of patient protection measures and of optimisation of procedures.

census unit population exposures to ambient air pollutants

BACKGROUND

Progress has been made recently in estimating ambient PM(2.5) (particulate matter with aerodynamic diameter < 2.5 μm) and ozone concentrations using various data sources and advanced modeling techniques, which resulted in gridded surfaces. However, epidemiologic and health impact studies often require population exposures to ambient air pollutants to be presented at an appropriate census geographic unit (CGU), where health data are usually available to maintain confidentiality of individual health data. We aim to generate estimates of population exposures to ambient PM(2.5) and ozone for U.S. CGUs.

METHODS

We converted 2001-2006 gridded data, generated by the U.S. Environmental Protection Agency (EPA) for CDC's (Centers for Disease Control and Prevention) Environmental Public Health Tracking Network (EPHTN), to census block group (BG) based on spatial proximities between BG and its four nearest grids. We used a bottom-up (fine to coarse) strategy to generate population exposure estimates for larger CGUs by aggregating BG estimates weighted by population distribution.

RESULTS

The BG daily estimates were comparable to monitoring data. On average, the estimates deviated by 2 μg/m(3) (for PM(2.5)) and 3 ppb (for ozone) from their corresponding observed values. Population exposures to ambient PM(2.5) and ozone varied greatly across the U.S. In 2006, estimates for daily potential population exposure to ambient PM(2.5) in west coast states, the northwest and a few areas in the east and estimates for daily potential population exposure to ambient ozone in most of California and a few areas in the east/southeast exceeded the National Ambient Air Quality Standards (NAAQS) for at least 7 days.

CONCLUSIONS

These estimates may be useful in assessing health impacts through linkage studies and in communicating with the public and policy makers for potential intervention.

The influence of location, source, and emission type in estimates of the human health benefits of reducing a ton of air pollution

The benefit per ton ($/ton) of reducing PM(2.5) varies by the location of the emission reduction, the type of source emitting the precursor, and the specific precursor controlled. This paper examines how each of these factors influences the magnitude of the $/ton estimate. We employ a reduced-form air quality model to predict changes in ambient PM(2.5) resulting from an array of emission control scenarios affecting 12 different combinations of sources emitting carbonaceous particles, NO(x), SO(x), NH(3), and volatile organic compounds. We perform this modeling for each of nine urban areas and one nationwide area. Upon modeling the air quality change, we then divide the total monetized health benefits by the PM(2.5) precursor emission reductions to generate $/ton metrics. The resulting $/ton estimates exhibit the greatest variability across certain precursors and sources such as area source SO(x), point source SO(x), and mobile source NH(3). Certain $/ton estimates, including mobile source NO(x), exhibit significant variability across urban areas. Reductions in carbonaceous particles generate the largest $/ton across all locations.