UNASSIGNED: Wildfires are increasing in magnitude, frequency, and severity. Populations in the wildland-urban interface and in downwind communities are at increased risk of exposure to elevated concentrations of fine particulate matter (PM2.5) and other harmful components of wildfire smoke. We conducted this analysis to evaluate the use of modeled predictions of wildfire smoke to create county-level measures of smoke exposure for public health research and surveillance.
UNASSIGNED: We evaluated four years (2015-2018) of grid-based North American Mesoscale (NAM)-derived PM2.5 forecasts from the U.S. Forest Service BlueSky modeling framework with monitoring data from the Environmental Protection Agency Air Quality System (AQS), the Interagency Monitoring of Protected Visual Environments (IMPROVE), the Western Regional Climate Center (WRCC), and the Interagency Real Time Smoke Monitoring (AIRSIS) programs. To assess relationships between model-derived estimates and monitor-based observations, we assessed Spearman\'s correlations by spatial (i.e., county, level of urbanization, states in the western United States impacted by major wildfires, and climate regions) and temporal (i.e., month and wildfire activity periods) characteristics. We then generated county-level smoke estimates and examined spatial and temporal patterns in total and person-days of smoke exposure.
UNASSIGNED: Across all counties in the coterminous United States and for all days, the correlation between county-level model- and monitor-derived PM2.5 estimates was 0.14 (p < 0.001). Correlations were stronger using data from temporary monitors and for areas and days impacted by high wildfire smoke, especially in the western United States. Correlations between county-level model- and monitor-derived estimates in non-metropolitan counties, and at higher concentrations ranged from 0.25 to 0.54 (p < 0.001).
UNASSIGNED: In general, public health practitioners and health researchers need to consider the pros and cons associated with modeled data products for conducting health analyses. Our results support the use of model-derived smoke estimates to identify communities impacted by heavy smoke events, especially during emergency response and for communities located near wildfire episodes.

Ecosystem services are strongly responsive to changes in land use intensity, especially for the service of water purification, which is highly sensitive to water pollutant emission. Increased nitrogen (N) application to cropland has potential impacts on the supply and demand for water purification through changes in land use intensity. However, there has been a lack of research focusing on the impacts of cropland N application on population exposure to water purification deficit and their cross-regional delivery network. Taking the Dongting Lake (DTL) Basin as an example, this study explored the spatial pattern of N exposure in the DTL Basin from 1990 to 2015 by integrating water purification deficit and population density. Changes in potential N exposure in 2050 were simulated based on population projection data from the Shared Socioeconomic Pathways (SSP1-5). N delivery pathways in the DTL Basin were clarified by constructing the N delivery network. The results showed that N exposure increased significantly with increasing N application in DTL Basin. The DTL surrounding area and lower reaches of the Xiangjiang River Basin had high increases of N exposure (50.2 % and 71.6 %) and high increases in N exposure due to increases in N application per unit (N influence coefficients exceeding 0.5). The lower reaches of the Xiangjiang River Basin with the highest population density had the smallest decrease in N exposure (1.4 %-11.1 %) in the SSP1-5 scenarios. During 1990-2015, the increase of N export to the DTL surrounding area was higher in the lower reach sub-basins of DTL Basin. N application had a stronger impact on N delivery processes in the lower reaches of DTL Basin. Managers should distribute N applications to basins with high N retention and low N export to the DTL surrounding area. This study confirmed the strong response of water purification deficit and its population exposure to N application, and provided decision-making guidelines for water quality enhancement in DTL Basin from a spatial planning perspective.

Quantifying flood risks through a cascade of hydraulic-cum-hydrodynamic modelling is data-intensive and computationally demanding- a major constraint for economically struggling and data-scarce low and middle-income nations. Under such circumstances, geomorphic flood descriptors (GFDs), that encompass the hidden characteristics of flood propensity may assist in developing a nuanced understanding of flood risk management. In line with this, the present study proposes a novel framework for estimating flood hazard and population exposure by leveraging GFDs and Machine Learning (ML) models over severely flood-prone Ganga basin. The study incorporates SHapley Additive exPlanations (SHAP) values in flood hazard modeling to justify the degree of influence of each GFD on the simulated floodplain maps. A set of 15 relevant GFDs derived from high-resolution CartoDEM are forced to five state-of-the-art ML models; AdaBoost, Random Forest, GBDT, XGBoost, and CatBoost, for predicting flood extents and depths. To enumerate the performance of ML models, a set of twelve statistical metrics are considered. Our result indicates a superior performance of XGBoost (κ = 0.72 and KGE = 82%) over other ML models in flood extent and flood depth prediction, resulting in about 47% of the population exposure to high-flood risks. The SHAP summary plots reveal a pre-dominance of Height Above Nearest Drainage during flood depth prediction. The study contributes significantly in comprehending our understanding of catchment characteristics and its influence in the process of sustainable disaster risk reduction. The results obtained from the study provide valuable recommendations for efficient flood management and mitigation strategies, especially over global data-scarce flood-prone basins.

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.

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.

Air pollution poses a critical public health threat around many megacities but in an uneven manner. Conventional models are limited to depict the highly spatial- and time-varying patterns of ambient pollutant exposures at the community scale for megacities. Here, we developed a machine-learning approach that leverages the dynamic traffic profiles to continuously estimate community-level year-long air pollutant concentrations in Los Angeles, U.S. We found the introduction of real-world dynamic traffic data significantly improved the spatial fidelity of nitrogen dioxide (NO2), maximum daily 8-h average ozone (MDA8 O3), and fine particulate matter (PM2.5) simulations by 47%, 4%, and 15%, respectively. We successfully captured PM2.5 levels exceeding limits due to heavy traffic activities and providing an \"out-of-limit map\" tool to identify exposure disparities within highly polluted communities. In contrast, the model without real-world dynamic traffic data lacks the ability to capture the traffic-induced exposure disparities and significantly underestimate residents\' exposure to PM2.5. The underestimations are more severe for disadvantaged communities such as black and low-income groups, showing the significance of incorporating real-time traffic data in exposure disparity assessment.

Land subsidence is a worldwide geo-environmental hazard. Clarifying the influencing factors of land subsidence hazards susceptibility (LSHS) and their spatial distribution are critical to the prevention and control of subsidence disasters. In this study, we selected natural and anthropogenic features or variables on LSHS and used the interpretable convolutional neural network (CNN) method to successfully construct a LSHS model in China. The model performed well, with AUC and F1-score testing set accuracies reaching 0.9939 and 0.9566, respectively. The interpretable method of SHapley Additive exPlanations (SHAP) was use to elucidate the individual contribution of input features to the predictions of CNN model. The importance ranking of model variables showed that population, gross domestic product (GDP) and groundwater storage (GWS) change are the three major factors that affect China\'s land subsidence. During year 2004-2016, an area of 237.6 thousand km2 was classified as high and very high LSHS, mainly concentrated in the North China Plain, central Shanxi, southern Shaanxi, Shanghai and the junction of Jiangsu and Zhejiang. There will be 333.82-343.12 thousand km2 of areas located in the high and very high LSHS in the mid-21st century (2030-2059) and 361.9-385.92 thousand km2 of areas in the late-21st century (2070-2099). Future population exposure to high and very high LSHS will be 252.12-270.19 million people (mid-21st century) and 196.14-274.50 million people (late-21st century), respectively, compared with the historical exposure of 210.99 million people. The proportion of future railway and road exposure will reach 14.63 %-14.89 % and 11.51 %-11.82 % in the mid-21st century, and 15.46 %-17.12 % and 12.35 %-13.11 % in the late-21st century, respectively. Our findings provide an important information for creating regional adaptation policies and strategies to mitigate damage induced by subsidence.

Effective risk management requires accurate assessment of population exposure to volcanic hazards. Assessment of this exposure at the large-scale has often relied on circular footprints of various sizes around a volcano to simplify challenges associated with estimating the directionality and distribution of the intensity of volcanic hazards. However, to date, exposure values obtained from circular footprints have never been compared with modelled hazard footprints. Here, we compare hazard and population exposure estimates calculated from concentric radii of 10, 30 and 100 km with those calculated from the simulation of dome- and column-collapse pyroclastic density currents (PDCs), large clasts, and tephra fall across Volcanic Explosivity Index (VEI) 3, 4 and 5 scenarios for 40 volcanoes in Indonesia and the Philippines. We found that a 10 km radius-considered by previous studies to capture hazard footprints and populations exposed for VEI ≤ 3 eruptions-generally overestimates the extent for most simulated hazards, except for column collapse PDCs. A 30 km radius - considered representative of life-threatening VEI ≤ 4 hazards-overestimates the extent of PDCs and large clasts but underestimates the extent of tephra fall. A 100 km radius encapsulates most simulated life-threatening hazards, although there are exceptions for certain combinations of scenario, source parameters, and volcano. In general, we observed a positive correlation between radii- and model-derived population exposure estimates in southeast Asia for all hazards except dome collapse PDC, which is very dependent upon topography. This study shows, for the first time, how and why concentric radii under- or over-estimate hazard extent and population exposure, providing a benchmark for interpreting radii-derived hazard and exposure estimates.
UNASSIGNED: The online version contains supplementary material available at 10.1007/s00445-023-01686-5.

In the context of global warming and rapid urbanization, pollen has become a significant public health concern for Chinese citizens. However, there is a paucity of epidemiological research on the impact of pollen on allergen-linked diseases, such as allergic rhinitis and asthma, in China. Using data from the Beijing Chaoyang Hospital between 2013 and 2019, which included allergic rhinitis and asthma incidence, meteorological records, and air pollution data, we employed a Generalized Additive Model (GAM) to examine the relationship between overall and type-specific pollen concentrations in relation to varying population exposures. We found that increased overall pollen concentrations significantly increased the risks of allergic rhinitis and asthma in diverse populations. Notably, the risk of allergic rhinitis was higher than that of asthma at equivalent pollen concentrations. Seasonal trends indicated that spring pollen peaks, primarily from trees, were associated with a lower risk of both allergic rhinitis and asthma than autumn peaks, predominantly from weeds. This study underscores the importance of identifying pollen species that pose heightened risks to different demographic groups across seasons, thereby providing targeted interventions for public health agencies.

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.