Regional Pollution Research Articles

Regional quality analysis of the hydrological environment with an improved random forest model based on the chimpanzee algorithm.

High-precision evaluations of water environment quality are highly important for improving the accuracy of early warning systems of regional water pollution risk and improving the regional water environment. This paper employs the chimp optimization algorithm (ChOA) to enhance the traditional random forest model, resulting in the chimp optimization algorithm-random forest (ChOA-RF) water quality assessment model for evaluating the Jiansanjiang area in Heilongjiang Province, China. The results show that the overall water environment in Jiansanjiang has the following characteristics: "The water quality of farms in the northwest is poor, and the quality of groundwater is better than that of surface water." Total nitrogen (TN) and total phosphorus (TP) in surface water and ammonium nitrogen (NH3-N), ferrum (Fe), and manganese (Mn) in groundwater are the main pollutants. The TP and TN in surface water and the NH3-N in groundwater exceeded the relevant standards, likely due to the excessive application of chemical fertilizers, especially nitrogen fertilizers. Additionally, Fe and Mn are harmful native substances. According to these findings, targeted improvement strategies, such as reducing nitrogen fertilizer application, plugging well, and increasing the surface water utilization rate, are proposed. Moreover, the ChOA-RF model is compared with the traditional empirical value model and the particle swarm optimization-random forest (PSO-RF) model. The results show that the ChOA-RF model can effectively reduce the root mean square error and mean absolute percentage error and improve the coefficient of determination. The running time and convergence ability are also better than those of the PSO-RF model, which is a more accurate and efficient machine learning model. The model can be used not only for high-precision evaluation of regional water environment quality but also for other machine learning fields.

Evolutionary game analysis and efficiency test of water pollution control driven by emission trading: Evidence from Zhejiang Province, China

Presently, China is actively endorsing the pilot initiative for the remunerative use and trading of emission. By examining the operation and efficacy of emission trading in the context of water pollution control, one can contribute to the advancement and refinement of this system, thereby facilitating the attainment of regional pollution reduction, carbon reduction, and high-quality development objectives. In pursuit of this objective, we develop a theoretical framework for the local government and sewage enterprises evolutionary game of water pollution control, which includes two scenarios without considering and considering emission trading for studying the influencing factors and evolution trajectory of the game subject's. Through the stability analysis, the game interactive mechanism, the difference in evolutionary trajectory, and the response logic of the decision-making body in different situations become clearly visible. Further, the system sensitivity factors are analyzed by solving the partial derivation of the area formula of the phase diagram. And the efficacy of the sewage trading system in water pollution control in Zhejiang Province is empirically examined at the micro level by adopting the trading data of the first pilot area of sewage trading in the country and the case of pollution control in Jinhua City. The research reveals the following conclusions: Under specific circumstances, emission trading can incentivize businesses and even industries to enhance pollution control measures as a whole. The performance and degree of sensitivity factors vary across gaming systems, with public reputation evaluation and central government inspection serving as positive constraints. The initial cost of paid use of emission permits, as a fixed cost component for firms to address pollution, has no effect on the enterprises' behavioral actions to satisfy emission regulations. The findings can furnish local governments with a theoretical foundation and decision support in order to optimize regulatory strategies and enhance pollution control policies.

The impact of evolving synoptic weather patterns on multi-scale transport and sources of persistent high-concentration ozone pollution event in the Yangtze River Delta, China

High-concentration ozone pollution pose threats to ecosystems and human health. However, there is limited research on the impact of the alternating evolution of synoptic weather patterns (SWPs) on the multi-scale transport processes and sources of ozone. From June 14 to 18, 2018, a rare consecutive ozone pollution plagued in Hefei and broader Yangtze River Delta region (YRD). This study investigates the meteorological factors and sources using in-situ observational data and WRF-Chem model simulations. Analysis reveals a northeastern low-pressure system moving from north to south generated a cold front. This moving cold front facilitated the vertical transport of warm air masses carrying high-concentration ozone originating from North China. Subsequently, Ozone-rich air masses (ORMs) were transported over the YRD, influenced by the eastward movement of the Mongolian high-pressure system. Based on WRF-Chem model with NOx tagging mechanisms and WRF-FLEXPART backward simulations, it is confirmed that a notable atmospheric transport originated from North China region (NCR) to Hefei, especially on June 15. As the Mongolian high-pressure weakens and shifts east-southward, it carried ORMs generated by NOx emissions from the YRD, accumulating over the sea within the range of 120°E to 126°E and 25°N to 30°N. Both WRF-chem model results and TRopospheric Ozone and Precursors from Earth System Sounding (TROPESS) Chemistry Reanalysis dataset Version 2 (TCR-2) revealed the existence of ORMs in this geographic range. Subsequently, the ORMs carried out to sea by the weakened high-pressure system were reintroduced inland, influenced by southeast winds brought about by the peripheral circulation of typhoon “Gaemi”. In summary, the alternating evolution of SWPs significantly influences multi-scale ozone transport from both the NCR and the YRD regions, making substantial contributions to this prolonged episode. These findings offer valuable insights for improving regional ozone pollution prevention and control mechanisms.

Characterization and Source Analysis of Heavy Metal(loid)s Pollution in Soil of an Industrial Park in Kunming, China

This study investigated the characteristics and sources of heavy metal(loid) pollution in the soil of a key industrial park in Kunming, China. In total, 60 soil samples (40 from agricultural land and 20 from construction land) were collected from and around the park. The soil pH and contents of Arsenic (As), lead (Pb), copper (Cu), zinc (Zn), cadmium (Cd), mercury (Hg), nickel (Ni), and chromium (Cr) were measured. The contents of the eight heavy metal(loid)s were analyzed using the background values of heavy metal(loid)s in the Kunming soil. The pollution load, geoaccumulation, and Nemero Comprehensive Pollution Indices were used for environmental risk evaluation. Cluster and principal component analyses were used to resolve heavy metal(loid) sources. Cd was enriched in construction and agricultural soils. As, Hg, Cd, Pb, Cu, and Zn exhibited large spatial differentiation and were significantly affected by the external environment. A regional pollution load index of 3.02 indicated overall heavy pollution. The pollution load index for each heavy metal(loid) indicated light pollution. The geoaccumulation index indicated relatively severe As, Cd, Cu, Pb, and Zn pollution. The Nemero Composite Pollution Index value showed that the study area was heavily polluted, with construction land being mildly polluted by Cd, and agricultural land being moderately polluted. The results of the spatial distribution show that there were high levels of contamination in the center. Correlation and principal component analyses showed that the pollution sources of the eight heavy metal(loid)s varied. Hg, Cd, and Pb originate primarily from industrial and agricultural pollution. Traffic sources significantly impacted Cu, Pb, Cd, and Cr. Natural sources are the main sources of Cr, Ni, and Cd. Ni is also affected by industrial sources, whereas Zn and Cu are affected by agricultural pollution. The influences of As, Cd, and Pb on the surface soil in the study area were more serious. Cd is more widely polluted and should be a priority in controlling soil heavy metal(loid)s.

Significance of anthropogenic black carbon in modulating atmospheric and cloud properties through aerosol-radiation interaction during a winter-time fog-haze

The present study investigated the aerosol-radiation-cloud interaction of anthropogenic black carbon (BC) during a severe fog-haze event by utilizing WRF-Chem, multi-satellite and in-situ observations, reanalyses datasets, and HYSPLIT model. The WRF-Chem model adequately captured the regional distribution of aerosol, cloud, and meteorological variables over the study domain. The maximum BC surface concentration (≥7 μg m−3) was predominantly evident over densely populated central and lower Indo-Gangetic Plain (IGP) regions. Although smoke aerosols usually persisted within 4 km above the surface at daylight hours, they reached as high as 6 km during nighttime, specifically over the central IGP regions. The heavily influenced areas by BC aerosols expanded to almost the entire landmass and the continental outflow region upon doubling the anthropogenic emission. The geographical distribution of maximum perturbations in net shortwave radiation flux at the surface closely matched the spatial patterns of the highest BC concentration, indicating a crucial role of BC in directly preventing the incoming sunlight from reaching the surface. Consequently, the reduced solar radiation at the ground might result in substantial surface cooling (between −0.3 and −0.5 °C), eventually prohibiting surface evapotranspiration and diminishing the outgoing sensible and latent heat fluxes. Also, more amplified cooling in the polluted atmosphere can prevent further development of the planetary boundary layer (PBL), increasing particle entrapment near the surface via an ‘aerosol-radiation-PBL’ feedback loop. However, the most striking contrast between the two scenarios (typical and polluted) was the prolonged and intensified warming (0.5–0.8 °C) in the mid-troposphere, causing a remarkable drop in moisture (more than two to three times) and hence burn-off of liquid cloud droplets due to the semi-direct effect of increased BC aerosols. The intense mid-tropospheric heating could remarkably enhance the updraft velocity, supporting the vertical transport of the additional moisture to higher altitudes, forming more ice clouds at night. Therefore, the current study highlighted the urgency of mitigating anthropogenic BC emissions in highly polluted regions since increased emissions could considerably worsen severe fog-haze conditions and influence both liquid water and ice clouds, depending on the atmospheric conditions.

Digital Economy, Technological Innovation, and Environmental Pollution in Different Regions: An Analysis Based on a Threshold Model

Digital Economy, Technological Innovation, and Environmental Pollution in Different Regions: An Analysis Based on a Threshold Model

Estimating black carbon levels using machine learning models in high-concentration regions

Black carbon (BC) is emitted into the atmosphere during combustion processes, often in conjunction with emissions such as nitrogen oxides (NOx) and ozone (O3), which are also by-products of combustion. In highly polluted regions, combustion processes are one of the main sources of aerosols and particulate matter (PM) concentrations, which affect the radiative budget. Despite the high relevance of this air pollution metric, BC monitoring is quite expensive in terms of instrumentation and of maintenance and servicing. With the aim to provide tools to estimate BC while minimising instrumentation costs, we use machine learning approaches to estimate BC from air pollution and meteorological parameters (NOx, O3, PM2.5, relative humidity (RH), and solar radiation (SR)) from currently available networks. We assess the effectiveness of various machine learning models, such as random forest (RF), support vector regression (SVR), and multilayer perceptron (MLP) artificial neural network, for predicting black carbon (BC) mass concentrations in areas with high BC levels such as Northern Indian cities (Delhi and Agra), across different seasons. The results demonstrate comparable effectiveness among the models, with the multilayer perceptron (MLP) showing the most promising results. In addition, the comparability between estimated and monitored BC concentrations was high. In Delhi, the MLP shows high correlations between measured and modelled concentrations during winter (R2: 0.85) and post-monsoon (R2: 0.83) seasons, and notable metrics in the pre-monsoon (R2: 0.72). The results from Agra are consistent with those from Delhi, highlighting the consistency of the neural network's performance. These results highlight the usefulness of machine learning, particularly MLP, as a valuable tool for predicting BC concentrations. This approach provides critical new opportunities for urban air quality management and mitigation strategies and may be especially valuable for megacities in medium- and low-income regions.

Mediation of Climate Anxiety in Doctors and Nurses: A Structural Equation Modelling Study

Aims. The goal of this meteorological psychology study is to understand the behavioral implications on the professional lives of medical professionals in one of the most polluted regions of the world with a compromised air quality index. There is a paucity of literature to explore climate ethics as to how behavioral aspects of morals may alter related to climate change. Methods. The study is a cross-sectional correlational quantitative study formulated through purposive sampling. Research took place during the smog season of the world’s most polluted city. The research instruments of Climate anxiety, pain and distress, and cognitive failure were administered to a sample of 211 medical professionals. Confirmatory factor analysis was conducted and resulted in robust model fitness with indices between .80 and .90. Item loadings for three factors were statistically ideal of greater than. 30. Barron and Kenny’s approach was applied to conduct mediation through path analysis in which climate anxiety significantly mediates between pain distress and cognitive failure (β = .51; p < .0001). Multiple regression results using bootstrap confidence showed that climate anxiety (β = .993; p < .0001), and pain distress result in cognitive failure. The independent sample t-test was carried out to explore if nurses and doctors are different in terms of experiencing climate anxiety, pain distress, and cognitive failure. Results. There is a strong significant association among climate anxiety, pain distress, and cognitive failure with strong to moderate correlation values, p < .001. Confirmatory factor analysis was conducted and resulted in robust model fitness with indices between .80 and .90. Item loadings for three factors were statistically ideal at .30. 102 Nurses (M = 85.216; SD = 4.94) were more painfully distressed, prone to cognitive failure, and have climate anxiety as compared to 109 doctors (M = 71.211; SD = 9.23).

The effects of the clean air actions on the Beautiful China initiative: The regional heterogeneity analysis

Improving air quality is one of the most important tasks of the Beautiful China initiative. Learning the air pollution situation and the effects of the clean air actions (CAAs) of China is meaningful for controlling the air pollution. Due to the diversity of air pollution, BTH, YRD and PRD were studied as the key areas, the regional pollution characteristics were analyzed firstly, and the correlation and heterogeneity of regions were studied by correlation coefficient method and the Theil index model. Moreover, the synergies in reducing pollution and carbon dioxide were also considered of the three regions. The air pollution of the three regions was imbalanced. O3 pollution has became widespread appearance since 2014, and from 2013 to 2016, PM2.5 pollution differences of the three regions has been narrowing. The scattered and dirty pollution control, comprehensive treatment of scattered coal pollution and clean heating made the BTH region have better synergistic effect of pollution control and carbon reduction. The collaborative control of O3 have more impacts on pollution control and carbon reduction. By analyzing, the most practical measures to improve the air pollution could be concluded in the different regions which would help the Beautiful China initiative of the regions.

Progress in Research on Microplastic Prevalence in Tropical Coastal Environments: A Case Study of the Johor and Singapore Straits

Microplastics are contaminants in marine ecosystems, posing great threats to biota and human health. In this work, we provide an overview of the progress made in understanding microplastic prevalence in tropical coastal environments, focusing on the Johor and the Singapore Straits as a case study. We examine the sources, distribution, transport, and ecological impact of microplastic pollution in this region through a systematic review. All papers relating to marine microplastics in Singapore’s sand and benthic sediments, seawater, and marine biota were used for analysis, from 2004 to 2023. In addition, we discuss the influence of envi-ronmental factors such as coastal morphology and anthropogenic activities on patterns of microplastic accumulation. We emphasize that microplastic pollution is more prevalent along the eutrophic Johor Strait compared to the Singapore Strait due to hydrological conditions. Rainfall is also a key factor that influences mi-croplastic abundance during the monsoon seasons. Furthermore, the bacterial and plankton assemblages of organisms on microplastic surfaces are diverse, with eutrophic waters enhancing the diversity of organisms on microplastic surfaces. Novel harmful cyanobacteria and bloom species of phytoplankton were also found on microplastic surfaces. By synthesizing existing research findings and highlighting regional characteristics, this paper contributes to ongoing efforts to mitigate microplastic pollution in tropical regions.

Early Warning of Regional Groundwater Pollution： A Case Study for Plain Area of Barkol-Yiwu Basin

Groundwater pollution early warning is an effective means for regional groundwater pollution prevention. The groundwater pollution early warning model coupled with the current situation of groundwater quality, groundwater quality variation trend, and groundwater pollution risk were applied to the plain area of Barkol-Yiwu Basin, and the regional scale groundwater pollution early warning was realized by combining the early warning of groundwater quality status and trend. The TOPSIS method based on comprehensive weight was used to evaluate the current situation of groundwater quality. The variation trend of groundwater quality was analyzed by calculating the trend interpolation results of 18 in-situ groundwater quality monitoring wells. The groundwater vulnerability map, groundwater pollution load map, and groundwater function value map were superimposed using the superposition index method to evaluate groundwater pollution risk. The results showed that the groundwater quality was good and relatively good, and the poor groundwater quality in some areas was mainly affected by the shallow groundwater depth and the large porosity of the vadose zone. Groundwater quality was stable from 2011 to 2022, mainly due to the leakage of wastewater generated by industries and agriculture into groundwater, resulting in the deterioration of groundwater quality in some areas. Groundwater pollution risk was generally low, and the dual effects of high vulnerability and high pollution load of groundwater led to local areas with high pollution risk. The early warning level of groundwater pollution was generally low, and the heavy and highly heavy warning areas accounted for 16.4% and 17.5% of the study area, respectively, mainly distributed in Xiamaya Township of Yiwu County and northern Santanghu Town, Dahongliuxia Township, and Dahe Town of Barkol County. The quaternary sediments exposed were mainly sandy pebbles, with developed pores and strong water permeability. The interception and adsorption capacity of pollutants were weak. Pollutants produced by industries, agriculture, and life easily leaked into groundwater aquifers, resulting in poor groundwater quality and high risk of groundwater pollution, which ultimately led to a high early warning level of groundwater pollution in some areas. The research on early warning of groundwater pollution provided an important theoretical basis for the development of groundwater pollution remediation.

Does the Asian summer monsoon play a role in the stratospheric aerosol budget of the Arctic?

Abstract. The Asian summer monsoon has a strong convectional component with which aerosols are able to be lifted up into the lower stratosphere. Due to usually long lifetimes and long-range transport aerosols remain there much longer than in the troposphere and are also able to be advected around the globe. Our aim of this study is a synergy between simulations by Chemical Lagrangian Model of the Stratosphere (CLaMS) and KARL (Koldewey Aerosol Raman Lidar) at AWIPEV, Ny-Ålesund in the Arctic, by comparing CLaMS results with exemplary days of lidar measurements as well as analyzing the stratospheric aerosol background. We use global three-dimensional Lagrangian transport simulations including surface origin tracers as well as back trajectories to identify source regions of the aerosol particles measured over Ny-Ålesund. We analyzed lidar data for the year 2021 and found the stratosphere generally clear, without obvious aerosol layers from volcanic eruptions or biomass burnings. Still an obvious annual cycle of the backscatter coefficient with higher values in late summer to autumn and lower values in late winter has been found. Results from CLaMS model simulations indicate that from late summer to early autumn filaments with high fractions of air which originate in South Asia – one of the most polluted regions in the world – reach the Arctic at altitudes between 360 and 380 K potential temperature. We found a coinciding measurement between the overpass of such a filament and lidar observations, and we estimated that backscatter and depolarization increased by roughly 15 % during this event compared to the background aerosol concentration. Hence we demonstrate that the Asian summer monsoon is a weak but measurable source for Arctic stratospheric aerosol in late summer to early autumn.

Low-condensation diesel use contributes to winter haze in cold regions of China

The application of low-condensation diesel in cold regions with extremely low ambient temperatures (−14 to −29 °C) has enabled the operation of diesel vehicles. Still, it may contribute to heavy haze pollution in cold regions during winter. Here we examine pollutant emissions from low-condensation diesel in China. We measure the emissions of elemental carbon (EC), organic carbon (OC), and elements, including heavy metals such as arsenic (As). Our results show that low-condensation diesel increased EC and OC emissions by 2.5 and 2.6 times compared to normal diesel fuel, respectively. Indicators of vehicular sources, including EC, As, lead (Pb), cadmium (Cd), chromium (Cr), nickel (Ni), and manganese (Mn), increased by approximately 20.2–162.5% when using low-condensation diesel. Seasonal variation of vehicular source indicators, observed at road site ambient environments revealed the enhancement of PM2.5 pollution by the application of low-condensation diesel in winter. These findings suggest that −35# diesel, a low-cetane index diesel, may enhance air pollution in winter, according to a dynamometer test conducted in laboratory. It raises questions about whether higher emissions are released if −35# diesel is applied to running vehicles in real-world cold ambient environments.

The impact of air pollution on the facial skin of Caucasian women using real-life pollutant exposure measurements.

To date, studies examining the effect of air pollution on skin characteristics have relied on regional pollution estimates obtained from fixed monitoring sites. Hence, there remains a need to characterize the impact of air pollution in vivo in real-time conditions. We conducted an initial investigation under real-life conditions, with the purpose of characterizing the in vivo impact of various pollutants on the facial skin condition of women living in Paris over a 6-month period. A smartphone application linked to the Breezometer platform was used to collect participants' individual exposures to pollutants through the recovery of global positioning system (GPS) data over a 6-month period. Daily exposure to fine particulate matter (PM 2.5µm and PM 10µm), pollen, and air quality was measured. Facial skin color, roughness, pore, hydration, elasticity, and wrinkle measurements were taken at the end of the 6-month period. Participants' cumulated pollutant exposure over 6 months was calculated. Data were stratified into two groups (lower vs. higher pollutant exposure) for each pollutant. 156 women (20-60 years-old) were recruited, with 124 women completing the study. Higher PM 2.5µm exposure was associated with altered skin color and increased roughness under the eye. Higher PM 10µm exposure with increased wrinkles and roughness under the eye, increased pore appearance, and decreased skin hydration. Exposure to poorer air quality was linked with increased forehead wrinkles and decreased skin elasticity, while higher pollen exposure increased skin roughness and crow's feet. This study suggests a potential correlation between air pollution and facial skin in real-life conditions. Prolonged exposure to PM, gases, and pollen may be linked to clinical signs of skin ageing. This study highlights the importance of longer monitoring over time in real conditions to characterize the effect of pollution on the skin.

Driving Factors of NOx Emissions in China: Insights from Spatial Regression Analysis

China’s rapid industrialization and urbanization have led to significant nitrogen oxide (NOx) emissions, contributing to severe atmospheric pollution. Understanding the driving factors behind these emissions is crucial for effective pollution control and environmental management. Therefore, this study is an attempt to provide insights into the influence of socioeconomic factors and explore spatial dependencies of NOx emissions in China in 2022 employing spatial regression models (SRMs). Among the SRMs considered, the spatial Durbin model (SDM) is identified as the most suitable for analyzing regional NOx emissions. The study highlights the importance of controlling electricity consumption and vehicle emissions for addressing air pollution in Chinese regions. Specifically, a one billion kilowatt-hour increase in electricity consumption leads to approximately 549.6 tons of NOx emissions, and an increase of 1000 vehicles in a region results in an average increase of 7113.4 tons of NOx emissions in the same region. Furthermore, per capita consumption expenditure (PCEXP) and research and development (R&D) expenditure exhibit negative direct and spillover impacts. Contrary to previous studies, this research finds that changes in urban population density do not have a significant direct or indirect effect on NOx emissions within the studied areas. Moreover, we conducted additional investigations to assess the effectiveness of government action plans in reducing NOx emissions. Specifically, we evaluated the impact of Phases 1 and 2 of the Clean Air Action Plan, launched in 2013 and 2018, respectively, on the socioeconomic drivers of NOx emissions. Therefore, the data were modeled for the years 2013 and 2017 and compared to the results obtained for 2022. The findings indicate that over the entire period (2013–2022), the emission controls mandated by the action plan resulted in significant reductions in the impact of many of the studied NOx drivers. In conclusion, based on the results, this study presents recommendations to mitigate NOx emissions.

Optimizing open-pit coal mining operations: Leveraging meteorological conditions for dust removal and diffusion

Dust pollution from Chinese open-pit coal mines (OPCMs) threatens the coexistence of resource development and environmental protection. This research introduces a new approach to designing OPCMs based on meteorological indicators for dust removal and diffusion. It analyzes the production, distribution, and dust emission features of large-scale OPCMs in China. The factors affecting dust dispersion and atmospheric pollution characteristics were also examined. The findings reveal a surge in the number and output of OPCMs, intensifying the conflict between resource development and environmental protection. Notably, over 80% of OPCMs are in arid and semi-arid regions, exacerbating the challenge. Microclimate effects, including circulation and inversion effects, further amplify dust pollution. Regional and seasonal dust pollution patterns were identified, with the southern region experiencing the highest pollution levels, followed by the northern and central regions. Seasonally, dust pollution exhibits the following pattern: winter > autumn > spring > summer. An alarming decline in atmospheric self-cleaning capacity over the past two decades underscores the pressing challenges ahead for dust control. The increase in air stagnation days/events highlights the urgency for effective dust prevention and control measures. This research suggests considering meteorological elements in OPCM design for dust control. Optimizing mining operations based on weather forecasts enables the utilization of natural conditions for effective dust prevention and control. The results provide insights for dust prevention and control in open-pit mines to foster green and climate-smart mining.

Burden and trends of infectious disease mortality attributed to air pollution, unsafe water, sanitation, and hygiene, and non-optimal temperature globally and in different socio-demographic index regions

BackgroundEnvironmental factors greatly impact infectious disease-related mortality, yet there's a lack of comprehensive global studies on the contemporary burden and trends. This study aims to evaluate the global burden and trends of infectious disease mortality caused by air pollution, unsafe water, poor sanitation, and non-optimal temperature across Socio-Demographic Index (SDI) regions from 1990 to 2019.MethodsThis observational study utilized data from the Global Burden of Diseases Study to examine mortality rates from infectious diseases attributed to environmental risk factors between 1990 and 2019, including air pollution, unsafe water, sanitation, handwashing facilities (UWSH), and non-optimal temperatures. Age-standardized mortality rates (ASMRs) and estimated annual percentage change (EAPC) were utilized to present infectious disease mortality, and its trajectory influenced by environmental risk factors over the years. Nonlinear regression was conducted to explore the association between the SDI and ASMRs across regions from 1990 to 2019.ResultsIn 2019, global infectious disease deaths linked to air pollution, UWSH, and non-optimal temperature reached a startling 2,556,992. Disease mortality varied widely across SDI regions, with the highest number of deaths due to air pollution and UWSH in Low SDI regions, and deaths from non-optimal temperature primarily in High SDI regions. Age disparities emerged, with children under five and the elderly most affected. However, an increasing mortality trend was observed among seniors (65–69, 75–79, and over 80) in High SDI regions due to enteric infections linked to UWSH. Globally, a consistent decrease in ASMR was seen from 1990 to 2019 for all diseases connected to these factors, except for respiratory infections linked to non-optimal temperature.ConclusionsOur study underscores the significant impact of air pollution, UWSH, and non-optimal temperatures on global infectious disease mortality, particularly among vulnerable groups such as children and the elderly. It's important to tackle these challenges with targeted interventions aiming to enhance environmental quality, improve water and sanitation systems, and control extreme temperatures. In addition, international cooperation is essential for bridging regional disparities and driving global public health initiatives forward, thereby helping achieve Sustainable Development Goals more effectively.

Intelligent Water Quality Testing Device based on Internet of Things Technology

Water quality testing is an important environmental testing standard. However, at present, Internet of Things water quality testing products generally have problems such as single function, complex interface, and short sensor life. The design of this paper is a device with intelligent water quality detection technology, including the independent design of the sensor interface standard, the sensor automatic recognition, plug and play; The sensor is equipped with a lifting mechanical structure to avoid long-term soaking of the sensor and increase the service life of the sensor by 35%. At the same time, combined with intelligent Internet of Things, big data analysis and other technologies, help relevant departments to grasp water quality information in a timely and accurate manner, early warning and forecasting of major regional water pollution accidents, supervision of pollutant discharge, and implementation of control systems to provide help. It is mainly used to monitor the water quality data of small and medium-sized reservoirs, rivers and lakes to achieve intelligent management within the jurisdiction.

Aerosol in global oceanic regions: Four-decade trends, spatial patterns, and policy implications

High aerosol loadings are observed not only in megacities on continents but also in oceanic regions like the Bohai Sea. This work provides a comprehensive analysis of the spatial and temporal variations in Aerosol Optical Depth (AOD) across different ocean regions worldwide over the past four decades, using remote sensing reanalysis data. The mean AOD value across all oceanic grids is approximately 0.112, with higher levels recorded in the Central Atlantic (~0.206), followed by the North Indian Ocean (~0.201), and the Western North Pacific (~0.197). A latitudinal analysis reveals that high AOD values are predominantly found in the Northern Hemisphere's oceanic regions, especially between latitudes 0° and 70° N. Except for the Gulf of California and Hudson Bay, AOD values in the other fourteen surveyed inland seas surpass the mean levels found at similar latitudes in oceanic regions. Among which, the Bohai Sea stands out as the most polluted oceanic region with AOD value of 0.35. Over the last four decades, AOD trends have revealed a significant decrease across about 89.5 % of global oceanic grids, while an increase in AOD is observed in low-latitude oceanic areas (30° S–30° N). Investigation into inland seas shows that nearly two-thirds have experienced a declining AOD trend, while sharply upward trends in AOD are primarily found in Asia. The Bohai Sea shows the largest increase in AOD, with an annual growth rate of 1.4 %. The turning-points of the AOD in each inland sea confirm the success of regional emission control policies initiated on the adjacent continents. To improve air quality in inland seas like the Bohai Sea, adjusting industrial layouts, such as relocating heavy industries from the surrounding coastal cities' proximities to areas near open seas, could significantly benefit public health.

Assessing aerosol-radiation interaction with WRF-chem-solar: Case study on the impact of the “pollution reduction and carbon reduction synergy” policy

Aerosol-radiation interaction (ARI) plays an important role in air quality and climate change. The implementation of the “Pollution Reduction and Carbon Reduction Synergy (PRCRS)” policy provides a great case to explore the impact of ARI on air quality in Beijing and the surrounding Beijing-Tianjin-Hebei (BTH) region. In this study, we analyzed the pollution characteristics and their relationship with meteorological factors in the BTH region, and used the WRF-Chem-Solar model to study the impact of the ARI effect on regional meteorological conditions, air quality improvement, and renewable energy during the implementation of atmospheric pollution improvement policies based on four different scenarios. The ARI effect led to decreases in 2-m temperature, planetary boundary layer height, surface downward shortwave radiation (SWDOWN), and an increase in 2-m relative humidity. The meteorological conditions were unfavorable to the dispersion of pollutants, and the ARI effect can lead to further aggravation of pollutants. The implementation of the PRCRS policy can suppress the adverse effects of ARI on regional air pollution diffusion conditions and improve regional pollution meteorological conditions, and the ARI effect can further enhance the policy's impact on improving air quality. In terms of renewable energy, the implementation of the PRCRS policy will increase renewable energy in the BTH region, due to the inhibition of the ARI effect on SWDOWN and wind speed at 100 m.