Population Exposure Risk Research Articles

Understanding the differences in the contribution and impact of urbanization on urban warming during heatwave and non-heatwave periods in China

Heatwaves (HW) present a significant threat to human health, making it essential to understand HW trends in China and the interaction between urbanization and HW is crucial for effective climate change adaptation. This study aims to quantify the differences in the contribution and impact of urbanization on urban warming during HW and non-heatwave (NHW) periods in China. By utilizing remote sensing impervious surface data, 2, 421 meteorological stations were dynamically classified into urban and rural stations from 1978 to 2017. The study identified concurrent heatwave (CHW) and NHW periods in both urban and rural areas, exploring the differences in urbanization effect between these periods. The results revealed that most regions in China were experiencing increasingly frequent and severe HW, with the number of HW events and total HW days rising by 0.3 events and 2.2 days per decade, respectively. During CHW periods, the average temperature difference between urban and rural areas increased by 0.33 °C, while the effect of urbanization on urban warming rose by 0.21 °C per decade, compared to NHW period. This indicates a synergistic enhancement of population exposure risk due to HW and urbanization. Over 60% of cities experienced a steeper linear trend in urban temperatures compared to rural areas during CHW, alongside reduced wind speeds and decreased precipitation. Implementing targeted adaptation and mitigation strategies is crucial to mitigating the increased risk of population exposure during HW events.

Integrated Approach for the Assessment of Real Risk of Population Exposure in Industrial Areas

Research goal is to identify an integrated approach for assessing real risk exposure of population located in areas of extractive industries. We focused on environmental data evaluation (diffusion model), figures and contextual analysis of active health surveillance data of the population (questionnaire, chemistry panel, spirometry and activation of a biobank). Environmental exposure data crossed with the calculation of the AQI (Air Quality Index) did not highlight any critical issues. A cohort of 600 locals subjected to health surveillance was analysed by calculating risk perception; a questionnaire aimed at assessing risk perception was administered to the population sample.All data (environmental and clinical) were georeferenced and analysed using GIS (Geographic Information System) software; study of contaminant distribution was carried out using a Q-GIS software shows average exposure conditions well below regulatory limits, with minor exceptions relative to concentrations of finer particulate matter (PM 2.5) and with reference only to the major urban centers; analysis of risk perception evince “incorrect” perception, with little confidence in relation to the environmental monitoring of the Regional Agencies.

Road resuspension PM2.5 from two vehicles driving in parallel by CFD method: The characteristics and population exposure

Particulate matter (PM) from the road resuspension dust of vehicles driving can deteriorate urban air quality. The goal of our study was to develop a discrete phase model and sliding grid technology to simulate the distribution characteristics of resuspension PM2.5 from vehicle road dust, and to analyze PM2.5 exposure of drivers and pedestrians caused by resuspension dust. The results indicated that the maximum height of PM2.5 concentration at the XZ section at 1–10 s varied in a zigzag pattern, with the lowest at 3.37 m at 1 s and the highest at 5.85 m at 2 s. PM2.5 concentration diffusion presented a "two lungs within one bracket" shape at Z = −0.03 m section and PM2.5 was gradually diffused from the tire to the two sides of the vehicle at 1–10 s. PM2.5 concentration at y = 5.5 m line of X = −15 m section gradually decreased with ground height increasing. The average PM2.5 concentration at Z = 1.25 m section at 1–10 s was 1.86, 3.17, 6.85, 8.85, 12.78, 14.69, 16.64, 18.96, 21.50, and 22.98 μg/m3, respectively. The average PM2.5 concentration was directly proportional to the change in road dust mass flow rate. The range of PM2.5 concentration of points a (left driver), b (right driver), c1 (pedestrian standing still for 3 m on right side of right vehicle) and c2 (pedestrian walking at a speed of 1.5 m/s) were 0–49.8, 0–183.0, 0–16.5, and 0–59.8 μg/m3, and the time of the maximum PM2.5 concentration for a, b, c1, and c2 was 5, 6.5, 4.5 and 5 s, respectively. These results indicated that the driver of point b had the highest exposure concentration within 1–10 s, which was 5.8 times that of the driver of point a, 7.7, and 2.9 times that of pedestrians of points c1 and c2. These results can provide basic data for road dust emissions and population exposure risk management.

Assessment of Soil Wind Erosion and Population Exposure Risk in Central Asia’s Terminal Lake Basins

Assessment of Soil Wind Erosion and Population Exposure Risk in Central Asia’s Terminal Lake Basins

Extreme climate events and future population exposure under climate change in the Huaihe River basin of China based on CMIP6 multimodel ensembles projections

AbstractThe Huaihe River basin (HRB) of China located in the climate transition zone between warm temperate and subtropical areas is highly sensitive to climatic change. However, the changes in future climate extreme events under anthropogenic warming and the population exposure to these climate extremes in HRB remain unexplored. Here, using the eight commonly used extreme climate indices and based on the bias‐corrections of 16 global climate models (GCMs) in CMIP6, we present a projection and uncertainty analysis of extreme events and investigate the corresponding population exposure risk in HRB under three shared socioeconomic pathways (SSP1‐2.6, SSP2‐4.5, SSP5‐8.5). The 16‐GCM ensemble mean projects an evident warming trend under all three scenarios with a total increase of 25.6–68.0 days in summer days (>25°C) by the end of the century in HRB. Larger increases (decreases) in maximum and minimum temperatures (frost days) are projected in the western HRB. Very heavy rain days (R20mm), maximum 5‐day precipitation (RX5day) and simple daily intensity index (SDII) will experience intensification across most of HRB (especially in southern and western HRB). The consecutive dry days is projected to decrease in northwestern HRB and increase in southern HRB. However, there is a large spatial variability in GCM uncertainty with a higher SSP scenario generally having higher uncertainty. Increases in summer days and R20mm exacerbate population exposure in HRB in near future (2030–2059), but in far future (2070–2099) although summer days (R20mm) continues to rise, population exposure is expected to decrease due to the rapid decline in population density.

Population heat exposure risk from the perspective of urban heat island spatial expansion in China during 2005–2020

The combined impact of global climate change and accelerated urbanization has intensified the urban heat island (UHI) effect, heightening population heat exposure risk. This study integrated MODIS LST (Land Surface Temperature), LUCC (Land Use Cover Change), GUB (Global Urban Boundaries), DEM (Digital Elevation Model), and population data to identify the spatial expansion of UHI in China from 2005 to 2020. A population-weighted urban heat island intensity (UHII) index (Ep) was proposed based on gridded UHII and population to compare the population heat exposure risk for the newly developed and pre-existing UHI under three levels of UHII. In 2020, UHI areas doubled since 2005, with infilling type exhibiting the highest UHII, while edge-expansion type was largest, and leapfrogging type had the lowest UHII. In the summer of 2020, the Ep indices in China were 1.75 °C and 1.79 °C during daytime and nighttime, respectively, representing increases of 0.12 °C and 0.44 °C compared to 2005. For the newly developed UHI areas with UHII exceeding 2 °C, infilling area exhibited greater daytime population heat exposure risk, whereas leapfrogging area showed higher nighttime risk. This study established a connection between UHI spatial expansion and population exposure risk, offering the insights for mitigating urban environmental hazards in the future.

Spatio-temporal patterns and population exposure risks of urban heat island in megacity Shanghai, China

Accompanied by global warming and rapid urbanization, the urban heat island (UHI) effect has been intensifying, which leads to an increased risk of population heat exposure, especially in mega-cities. However, there are fewer studies on the identification of UHI ranges and the associated population heat exposure risks, particularly on the fine scale. Therefore, this study took Shanghai as a case study and explored the spatio-temporal evolution characteristics of the UHI ranges and population heat exposure risks, based on five-period MODIS land surface temperature (LST) product, population density distribution data, and land use and cover change data from 2000 to 2020, combined with the temperature attenuation mutation (TAM) method, breakpoint regression model, population heat exposure relative risk assessment method, and spatial autocorrelation analysis. The results showed that: (1) From 2000 to 2020, the UHI range in Shanghai was continuously expanding, and the intensity of the surface UHI was gradually increasing. The overall pattern was characterized by a stable large heat island in the central urban area and new small heat islands that continued to emerge in the peripheral suburbs. (2) The population heat exposure risks in Shanghai intensified from 2000 to 2020, with an increasing number of people exposed to high-intensity UHI ranges, doubling the population coverage in these areas. By the end of 2020, the population exposed to UHI ranges accounted for approximately 81 % of the total population of Shanghai. (3) The spatial pattern of population heat exposure risks from 2000 to 2020 was generally similar. Population heat exposure regions of high risk level were primarily concentrated in the central urban areas, showing typical spatial clustering characteristics, while population heat exposure regions of low risk level were mainly dispersed in the peripheral suburbs, and showed a gradual transformation trend into population heat exposure regions of high risk level. Our findings can provide scientific basis and decision support for precise and refined urban heat risk control and planning management.

Investigating the Impact of Green Space Ratio and Layout on Bioaerosol Concentrations in Urban High-Density Areas: A Simulation Study in Beijing, China

The COVID-19 pandemic significantly impacted global development. Through bioaerosols emitted by human respiration, respiratory infectious diseases, including COVID-19, are transmitted. The bioaerosol concentrations can be affected by the urban climate and morphology. However, the effects of urban green spaces on bioaerosol concentrations remain unclear. Focusing on the dormitory area of Beijing Forestry University, this study first investigated the influence of different green space ratios on the average bioaerosol concentrations using the ENVI-met software. Moreover, both overall and local green space layouts were analyzed for their impact on bioaerosol concentrations. The results indicated that ventilation conditions were the primary factor influencing bioaerosol concentrations. During peak congestion, a 10% increase in the green space ratio resulted in a 2% rise in the average bioaerosol concentration. Furthermore, a distributed layout resulted in a 1.3% higher average bioaerosol concentration than a concentrated layout with an equivalent green space ratio. Enacting strategies such as Roadside Green Spaces Retreat, Road Spaces Expansion, and Intersection Green Spaces Chamfering led to reductions in local bioaerosol concentrations by up to 17.7%, 18.44%, and 12.69%, respectively. This study highlights the importance of adjusting green space layouts in urban high-density areas after the pandemic, reducing the risk of population exposure to bioaerosol concentrations.

The population exposure risk of urban heat island effect: From the perspective of urban spatial expansion in China

Urban spatial expansion is a critical aspect of urbanization, but it must occur in a manner that ensures the sustainable development of the urban climate. The primary objective of this study was to assess the population exposure risk of urban heat island (UHI) effect from the perspective of urban spatial expansion. Additionally, the population weighted urban heat island intensity (UHII) index (Ep) was proposed to compare the population exposure risk of UHI effects in different types of urban spatial expansion in China during the summer of 2005–2020. It was found that, in China, urban land increased by an area of 74,894 km2 (1.01-fold) from 2005 to 2020. Leapfrogging emerged as the predominant pattern of urban spatial expansion and was primarily driven by the conversion of cropland to urban land. It witnessed a significant increase in the area covered by UHIs of 107,119 km2 (1.20-fold), accompanied by a rise in the UHII of 0.12 °C in China. Notably, the UHII for infilling urban expansion was significantly higher at 2.03 °C, in contrast to 1.66 °C for edge expansion and 1.47 °C for leapfrogging. Correspondingly, the Ep was markedly larger under the infilling urban expansion (2.10 °C) in contrast to edge expansion (1.75 °C) and leapfrogging (1.67 °C). However, edge expansion dominated the newly developed urban land, contributing to 18.60 % of the Ep, which was still far less than the contribution of the pre-existing urban land to Ep (61.24 %). Importantly, the dominant driver of the increased Ep was the increasing UHII. This study established a connection between inevitable urban spatial expansion and the population exposure risk of UHI effect, providing a strong theoretical basis and practical guidance for the active adaptation and mitigation of urban environmental hazards in urban regions.

Modeling the Subpixel Land-Use Dynamics and Its Influence on Urban Heat Islands: Impacts of Factors and Scale, and Population Exposure Risk

Urban heat islands (UHIs) has been proven firmly related to the land use structure. Identifying interactions between UHIs and multiple land use components is a crucial step to obtain human heat welfare information. However, few studies have predicted sub cell scale land use structure dynamics on UHIs due to the lack of subpixel simulation methods. Herein, we present an integrated framework coupling subpixel unmixing and mixed-cell simulation methods to predict fine-scale land-use structural changes. A widely used XGBoost was used to determine the optimal scale for future UHIs prediction. This framework explores how multiscale changes in land use structure will affect future UHIs intensity, taking Wuhan, China as a study area. The results reveal that most influence comes from the scale below the 330-m grid, while the fine-scale land use components of a given position show limited impact on the UHI intensity. Impervious surfaces contribute more than 55% of the importance, while bare soil and water components within the 270-m grid also significantly affect UHIs. We also find that optimizing the structure of land use components can potentially release approximately 599,000 people from high-UHI regions in the study area.

Future projections of compound temperature and precipitation extremes and corresponding population exposure over global land

Extreme climate events are hotspots in global change. However, research on the changes in future compound events and population exposure is still limited. Leveraging from the data of the sixth phase of the Coupled Model Intercomparison Project (CMIP6), this paper aims to analyze the temporal and spatial changes of global compound temperature and precipitation extreme events in the future. We also predict the risk of population exposure and quantify the contribution of different factors to exposure. The results show that: (1) In the next 80 years, compound hot-dry event (CHDE) will increase at a rate of 0.02, 0.03, and 0.08 days per decade under the three scenarios of SSP1–2.6, SSP2–4.5 and SSP5–8.5, respectively. By comparison, compound hot-humidity event (CHHE) shows a downward trend under the three scenarios, with a downward rate of 0.01, 0.02, and 0.11 days per decade, respectively. (2) Under the SSP1–2.6 and SSP2–4.5 scenarios, CHDE and CHHE have two or more mutation points. Under the SSP5–8.5 scenario, CHDE shows a significant upward and CHHE shows a significant downward trend in the middle and late 21st century. These two indices exhibit periodic changes in all three scenarios (3) South Asia, West Asia, and Northeast Africa have higher CHDE values, while regions with higher CHHE values are located in North Asia and Greenland. (4) Climate change is a major factor affecting population exposure. For CHDE, climate, population, and their synergistic effects contribute about 75%, 20%, and 5% to the exposure, respectively. For CHHE, the contributions of these three factors are 85%, 10%, and 5% respectively. These findings provide scientific guidance for the rational formulation of population policies, the effective avoidance of climate disaster risks and the protection of human health.

Serological surveillance reveals a high exposure to SARS-CoV-2 and altered immune response among COVID-19 unvaccinated Cameroonian individuals.

Surveillance of COVID-19/SARS-CoV-2 dynamics is crucial to understanding natural history and providing insights into the population's exposure risk and specific susceptibilities. This study investigated the seroprevalence of SARS-CoV-2 antibodies, its predictors, and immunological status among unvaccinated patients in Cameroon. A multicentre cross-sectional study was conducted between January and September 2022 in the town of Douala. Patients were consecutively recruited, and data of interest were collected using a questionnaire. Blood samples were collected to determine Immunoglobin titres (IgM and IgG), interferon gamma (IFN- γ) and interleukin-6 (IL-6) by ELISA, and CD4+ cells by flow cytometry. A total of 342 patients aged 41.5 ± 13.9 years were included. Most participants (75.8%) were asymptomatic. The overall crude prevalence of IgM and IgG was 49.1% and 88.9%, respectively. After adjustment, the seroprevalence values were 51% for IgM and 93% for IgM. Ageusia and anosmia have displayed the highest positive predictive values (90.9% and 82.4%) and specificity (98.9% and 98.3%). The predictors of IgM seropositivity were being diabetic (aOR = 0.23, p = 0.01), frequently seeking healthcare (aOR = 1.97, p = 0.03), and diagnosed with ageusia (aOR = 20.63, p = 0.005), whereas those of IgG seropositivity included health facility (aOR = 0.15, p = 0.01), age of 40-50 years (aOR = 8.78, p = 0.01), married (aOR = 0.21, p = 0.02), fever (aOR = 0.08, p = 0.01), and ageusia (aOR = 0.08, p = 0.01). CD4+, IFN-γ, and IL-6 were impaired in seropositive individuals, with a confounding role of socio-demographic factors or comorbidities. Although the WHO declared the end of COVID-19 as a public health emergency, the findings of this study indicate the need for continuous surveillance to adequately control the disease in Cameroon.

High-resolution estimation of near-surface ozone concentration and population exposure risk in China.

Considering the spatial and temporal effects of atmospheric pollutants, using the geographically and temporally weighted regression and geo-intelligent random forest (GTWR-GeoiRF) model and Sentinel-5P satellite remote sensing data, combined with meteorological, emission inventory, site observation, population, elevation, and other data, the high-precision ozone concentration and its spatiotemporal distribution near the ground in China from March 2020 to February 2021 were estimated. On this basis, the pollution status, near-surface ozone concentration, and population exposure risk were analyzed. The findings demonstrate that the estimation outcomes of the GTWR-GeoiRF model have high precision, and the precision of the estimation results is higher compared with that of the non-hybrid model. The downscaling method enhances estimation results to some extent while addressing the issue of limited spatial resolution in some data. China's near-surface ozone concentration distribution in space shows obvious regional and seasonal characteristics. The eastern region has the highest ozone concentrations and the lowest in the northeastern region, and the wintertime low is higher than the summertime high. There are significant differences in ozone population exposure risks, with the highest exposure risks being found in China's eastern region, with population exposure risks mostly ranging from 0.8 to 5.

Route Optimization of Hazardous Material (Hazmat) Transportation with Risk Equity and Time-Varying Risk

This paper strives for route optimization of hazardous material (hazmat) for given probabilities and consequences. We aim to not only develop a model to determine the route with the least total risk and cost, but also pursue risk equity from three perspectives of government, enterprises, and society in the route optimization. Specifically, this paper reduces the differences of population exposure risk among road segments by using the threshold setting method and decreases the differences of environmental risk by minimizing the risk compensation cost caused by the additional environmental risk exceeding its average for each road segment. In addition, this paper analyzes the impact of the time-varying characteristics of risk and risk equity and proposes a multi-objective route optimization model which considers the minimization of total risk, cost, and risk differences under time-varying conditions. Finally, the ε-constraint method and an improved genetic algorithm are used to optimize the model, which is applied to a case study involving hazardous materials transportation route optimization in Shanghai, China. The resulting analyses indicate that risk equity can be improved by setting a proper threshold to acquire feasible paths and by minimizing the compensation cost to control the differences of environmental risk. Considering variance of time in the route optimization may lead to a route with lower risk and better risk equity.

Assessing urban population exposure risk to extreme heat: Patterns, trends, and implications for climate resilience in China (2000–2020)

Urban residents face serious thermal health risks from extreme heat owing to the cumulative effects of urbanization and climate warming. However, the patterns of urban populations exposed to extreme heat and urban extreme heat exposure risks (UEHER) require clarification in China. We determined urban extreme heat and the exposed populations for land surface temperature (LST) in 320 cities from 2000 to 2020 by setting thresholds and assessed the UEHER in China through the “Hazard-Exposure-Vulnerability” framework. Our findings indicated an average extreme LST threshold of 35.24 °C, varying from 29.32 °C to 47.79 °C. Higher extreme LST thresholds were mainly concentrated in Northwest China and developed cities. From 2000 to 2020, the extreme heat-exposed areas have increased by approximately 27.8 km2, equivalent to an average of approximately 321.3 soccer fields per year. The urban population exposed to extreme heat (UPEEH) in China has increased by approximately 115 million in the past 20 years, mainly in more developed cities, especially in eastern and northern China. Notably, the proportion of the UPEEH has decreased more rapidly in the east region. The UEHER index increased 52.5 % over 20 years, with 97.19 % of the cities having a worsening trend. Medium-developed and high-population density regions faced the dual risk of a high UEHER and more rapid increase in extreme heat. In contrast, less-developed regions faced the dual problems of high UEHER and low gross domestic product (GDP). Understanding vulnerable and prioritized areas of urban heat exposure will provide information for the development of adaptive policies that enhance urban climate resilience.

Future Projections of Heat Waves and Associated Mortality Risk in a Coastal Mediterranean City

Climate change has been linked to the escalating frequency, duration, and intensity of heat waves in the Mediterranean region, intensifying health concerns for the general populace. Urban environments face elevated health risks due to concentrated populations and the urban heat island effect, further amplifying nighttime heat conditions. This study aims to project changes in heat wave characteristics and the associated population exposure risk in a large Mediterranean city, Thessaloniki, Greece. High-resolution climate simulations, using the WRF model, were conducted for three 5-year periods (2006–2010, 2046–2050, 2096–2100) under the RCP8.5 emission scenario, covering Thessaloniki with a 2 km grid. By the end of the century, Thessaloniki is projected to experience over 60 annual heat wave days, compared to ~8 in the present climate, while some episodes were found to persist beyond 30 days. The relative risk during heat wave days is expected to rise, which is primarily due to nighttime heat stress. Interestingly, the results indicate that minimum apparent temperature might be a more reliable indicator in predicting heat-related mortality compared to maximum apparent temperature. These findings emphasize the growing importance of informed heat mitigation and adaptation strategies and healthcare preparedness in urban areas facing escalating heat-related health challenges.

Policy implications for synergistic management of PM2.5 and O3 pollution from a pattern-process-sustainability perspective in China

In recent years, the pattern of air pollution in China has changed profoundly, and PM2.5 and surface ozone (O3) have become the main air pollutants affecting the air quality of cities and regions in China. The synergistic control of the two has become the key to the sustainable improvement of air quality in China. In this study, we investigated and analyzed the spatial and temporal distribution patterns, exposure health risks, key drivers, and sustainable characteristics of PM2.5 and O3 concentrations in China from 2013 to 2022 at the national and city cluster scales by combining methodological models such as spatial statistics, trend analysis, exposure-response function, Hurst index, and multi-scale geographically weighted regression (MGWR) model. Ultimately, a synergistic management system for PM2.5 and O3 pollution was proposed. The results showed that: (1) The PM2.5 concentration decreased at a rate of 1.45 μg/m3 per year (p < 0.05), while the O3 concentration increased at a rate of 2.54 μg/m3 per year (p < 0.05). The trends of the two concentrations showed significant differences in spatial distribution. (2) Population exposure risks to pollutants showed an increasing trend, with PM2.5 and O3 increasing by 55.1 % and 42.7 %, respectively. The annual deaths associated with exposure to PM2.5 and O3 demonstrated a decreasing and inverted U-shaped trend, respectively, with annual average deaths of 1.312 million and 98,000. Significant regional disparities in health risks from these pollutants were influenced by socio-economic factors such as industrial activities and population density. In the future, it is expected that more than half of China's regions will be exposed to rising risks of PM2.5 and O3 population exposure. (3) Key drivers of regional exacerbation in PM2.5 and O3 levels include the number of industrial enterprises above designated size (NSIE) and population agglomeration (PA), while the disposable income of urban residents (URDI), technological innovation (TI), and government attention level (GAL) emerged as primary factors in controlling pollution hotspots, ranked in order of influence from greatest to least as TI > GAL > URDI. Overall, this study sheds light on the current status of air pollution and health risk sustainability in China and enhances the understanding of future air pollution dynamics in China. The results of the study may help to develop effective targeted control measures to synergize the management of PM2.5 and O3 in different regions.

Research on the spatial and temporal patterns of ozone concentration and population health effects in the Central Plains Urban Agglomeration from 2017 to 2020.

Fine particulate matter (PM2.5) and near-surface ozone (O3) are the main atmospheric pollutants in China. Long-term exposure to high ozone concentrations adversely affects human health. It is of great significance to systematically analyze the spatiotemporal evolution mechanism and health effects of ozone pollution. Based on the ozone data of 91 monitoring stations in the Central Plains Urban Agglomeration from 2017 to 2020, the research used Kriging method and spatial autocorrelation analysis to investigate the spatiotemporal variations of ozone concentration. Additionally, the study assessed the health effects of ozone on the population using the population exposure risk model and exposure-response relationship model. The results indicated that: (1) The number of premature deaths caused by ozone pollution in the warm season were 37,053 at 95% confidence interval (95% CI: 28,190-45,930) in 2017, 37,685 (95% CI: 28,669-46,713) in 2018, and 37,655 (95% CI: 28,647-46,676) in 2019. (2) The ozone concentration of the Central Plains urban agglomeration showed a decreasing trend throughout the year and during the warm season from 2017 to 2020, there are two peaks monthly, one is June, and the other is September. (3) In the warm season, the high-risk areas of population exposure to ozone in the Central Plains Urban Agglomeration were mainly concentrated in urban areas. In general, the population exposure risk of the south is lower than that of the north. The number of premature deaths attributed to ozone concentration during the warm season has decreased, but some southern cities such as Xinyang and Zhumadian have also seen an increase in premature deaths. China has achieved significant results in air pollution control, but in areas with high ozone concentrations and high population density, the health burden caused by air pollution remains heavy, and stricter air pollution control policies need to be implemented.

Identification of biological indicators for human exposure toxicology in smart cities based on public health data and deep learning.

With the acceleration of urbanization, the risk of urban population exposure to environmental pollutants is increasing. Protecting public health is the top priority in the construction of smart cities. The purpose of this study is to propose a method for identifying toxicological biological indicators of human exposure in smart cities based on public health data and deep learning to achieve accurate assessment and management of exposure risks. Initially, the study used a network of sensors within the smart city infrastructure to collect environmental monitoring data, including indicators such as air quality, water quality, and soil pollution. Using public health data, a database containing information on types and concentrations of environmental pollutants has been established. Convolutional neural network was used to recognize the pattern of environmental monitoring data, identify the relationship between different indicators, and build the correlation model between health indicators and environmental indicators. Identify biological indicators associated with environmental pollution exposure through training optimization. Experimental analysis showed that the prediction accuracy of the model reached 93.45%, which could provide decision support for the government and the health sector. In the recognition of the association pattern between respiratory diseases, cardiovascular diseases and environmental exposure factors such as PM2.5 and SO2, the fitting degree between the model and the simulation value reached more than 0.90. The research design model can play a positive role in public health and provide new decision-making ideas for protecting public health.

Estimate of near-surface NO2 concentrations in Fenwei Plain, China, based on TROPOMI data and random forest model.

Nitrogen dioxide (NO2) concentration is a crucial indicator of ground-level air quality, and elevated concentrations can adversely affect human health and the atmospheric environment. In this study, we utilized Tropospheric Monitoring Instrument (TROPOMI) tropospheric NO2 vertical column density data (VCD) and multi-source geographic data to establish a random forest regression (RF) model that accurately estimates NO2 concentrations near the ground in the Fenwei Plain. The model addresses the inherent limitations of traditional ground-based monitoring and provides data support for analyzing regional pollution spatial and temporal characteristics. (1) The RF model based on TROPOMI and geographic data demonstrates high estimation accuracy, with monthly average RF model fit and validation coefficient of determination (R2) reaching 0.949 and 0.875, respectively. (2) A complex nonlinear relationship exists between near-surface NO2 concentration and multi-source geographic data. The RF model's estimations reveal clear seasonal and regional variations in near-surface NO2 concentration. Concentrations are generally highest in winter, followed by spring and autumn, and lowest in summer. The high NO2 concentrations are primarily mainly distributed in the plains and river valleys with low elevation and dense population density. The model estimation results also indicate that the estimated effect is better when the NO2 concentration fluctuates less and anthropogenic emission reduction measures significantly impact the NO2 concentration near the ground. (3) The population exposure risk results indicate that most cities in the Fenwei Plain face varying exposure risks. These findings offer valuable insights for regional NO2 pollution management.