Introduction: Within the context of the yearly improvement of particulate matter (PM) pollution in Chinese cities, Surface ozone (O₃) concentrations are increasing instead of decreasing and are becoming the second most important air pollutant after PM. Long-term exposure to high concentrations of O₃ can have adverse effects on human health. In-depth investigation of the spatiotemporal patterns, exposure risks, and drivers of O₃ is relevant for assessing the future health burden of O₃ pollution and implementing air pollution control policies in China.Methods: Based on high-resolution O₃ concentration reanalysis data, we investigated the spatial and temporal patterns, population exposure risks, and dominant drivers of O₃ pollution in China from 2013 to 2018 utilizing trend analysis methods, spatial clustering models, exposure-response functions, and multi-scale geographically weighted regression models (MGWR).Results: The results show that the annual average O₃ concentration in China increased significantly at a rate of 1.84 μg/m³/year from 2013 to 2018 (160 μg/m³) in China increased from 1.2% in 2013 to 28.9% in 2018, and over 20,000 people suffered premature death from respiratory diseases attributed to O₃ exposure each year. Thus, the sustained increase in O₃ concentrations in China is an important factor contributing to the increasing threat to human health. Furthermore, the results of spatial regression models indicate that population, the share of secondary industry in GDP, NOx emissions, temperature, average wind speed, and relative humidity are important determinants of O₃ concentration variation and significant spatial differences are observed.Discussion: The spatial differences of drivers result in the spatial heterogeneity of O₃ concentration and exposure risks in China. Therefore, the O₃ control policies adapted to various regions should be formulated in the future O₃ regulation process in China.

Surface PM_2.5 concentrations in India have increased dramatically as emissions have risen in recent years. The role of meteorological factors in this increase is unclear, mainly due to a lack of long-term observations over the region. A 12-member ensemble of historical (1950–2014) simulations from the Community Earth System Model version 2-Whole Atmosphere Community Climate Model version 6 (CESM2-WACCM6) offers an unprecedented opportunity to examine simulated PM_2.5 and meteorology for 20th century climates that can arise due to ‘climate noise’ under the same historical greenhouse gas and air pollutant emission trajectories. CESM2-WACCM6 includes interactive aerosol and gas-phase chemistry in the atmosphere coupled to ocean-sea ice-land models, and each ensemble member differs only in its initial conditions of the climate state. We systematically examine, decade-by-decade, the changes in PM_2.5 and associated meteorology, including wind speed, surface temperature inversions, boundary layer height, precipitation, and relative humidity in four cities in India: Chennai, Kolkata, Mumbai, and New Delhi. Forced changes clearly emerge in meteorological variables from 1950 to 2014, including increases in both relative humidity and temperature inversion strength, and decreases in boundary layer height and average surface wind speed. The timing of these changes varies by city: boundary layer heights decrease most over New Delhi in the premonsoon season (ensemble average decrease of 400 m), but over Mumbai in the postmonsoon season (ensemble average decrease of 100 m). PM_2.5 concentrations increase across India regardless of climate variability, with an almost threefold increase from 1950 to 2014 over New Delhi. Analysis of dimensionless variables shows that PM_2.5 exhibits larger ensemble mean trends and smaller variability than the trends in the meteorological variables, enabling us to infer that the increase in PM_2.5 is predominantly controlled by increases in anthropogenic emissions rather than climate variability. Overall, our simulations corroborate the dominant role of air pollutant emissions on poor air quality in India.

This study aimed to explain the reasons for the differences in the PM_2.5 and PM₁₀ dust-retention capacity of different tree species. Ten typical landscape tree species with a strong ability to adsorb particulate matter and improve the quality of the atmospheric environment were selected in Zhangjiakou, and the leaves of each tree species were collected from April to October. The PM_2.5 and PM₁₀ dust-retention capacity of different tree species were measured using an aerosol regenerator. The differences in the leaf structure of different tree species were analyzed using an electron microscope. The results showed that the PM₁₀ and PM_2.5 per unit leaf area of 10 tree species ranged from 1.31 ± 0.68 to 2.64 ± 1.29 μg·cm⁻² and from 0.28 ± 0.13 to 0.99 ± 0.34 μg·cm⁻², and the PM₁₀ and PM_2.5 dust-retention capacity per unit leaf area of coniferous trees was higher than that of broad-leaved trees. Further, the PM₁₀ dust-retention capacity per unit leaf area of each tree species in different months was the highest in October (3.17 ± 1.12 μg·cm⁻²) and the lowest in August (0.79 ± 0.56 μg·cm⁻²). The PM_2.5 dust-retention capacity per unit leaf area was the highest in October (0.99 ± 0.34 μg·cm⁻²) and the lowest in April (0.28 ± 0.13 μg·cm⁻²). The annual PM₁₀ and PM_2.5 dust-retention capacity per hectare of Pinus tabulaeformis was the highest and that of Ginkgo biloba was the lowest. The conifer trees have rough leaves, and broad-leaved trees have smooth leaves. The leaves of P. tabulaeformis and Picea asperata have a widespread stomata distribution, and the leaf surface is not smooth, with a large number of grooves and bulges. The number of stomata on the leaf surface of Salix babylonica and G. biloba is less than that of P. tabulaeformis and P. asperata. When the dust-retention capacity of PM_2.5 per unit leaf area is high, the corresponding roughness is also significant, and a good logarithmic relationship exists between roughness and PM_2.5 per unit leaf area (R² = 0.9504). The results of this study might have an important reference value in terms of the selection of tree species with strong PM₁₀ and PM_2.5 dust-retention capacity and the improvement in ambient air quality in the northwest of Hebei Province.

The severe population ageing has rapidly increased the demand for urban elderly care services in most countries. As a novel urban elderly care mode, community-embedded elderly care facilities integrate various functions and allow older urban adults to enjoy comprehensive care services in a familiar environment at an acceptable cost. Therefore, it is widely recognised as an effective way to resolve the contradiction between the increasing demand and limited supply capacity of elderly care services in large cities. However, spatial analysis of elderly care facilities in previous studies were focused on static characters, ignoring the evolution process. The traditional static analysis methods might be one-sided for the spatial analysis of community-embedded elderly care facilities, considering their highly dynamic development. This study considers Beijing as a case study and establishes a novel spatiotemporal analytical framework, augmented by big data, to analyse the spatial distribution of the local community-embedded elderly care facilities (elderly stations) from a dynamic view. The multi-source data regarding elderly stations, the elderly population and basic geographic information of Beijing were extracted and integrated into the analysis. On this basis, the proposed framework was applied to examine the distribution tendency, evolution trend and accessibility of elderly stations from 2017 to 2020. The results reveal a significant cluster development characteristic of elderly stations. Although the density of elderly stations in the downtown area is much higher than that in the urban periphery, the elderly stations might still be unable to satisfy the enormous elderly care demand in Xicheng and Dongcheng districts. Moreover, the imbalance between the urban centre and peripheries and the spatial mismatch between the elderly stations and population were identified. The research output could support the planning practice of elderly stations for relevant departments.

This study aims to analyze the spatiotemporal distribution and evolution of digestive tract cancer (DTC) in Lujiang County, China by using the geographic information system technology. Results of this study are expected to provide a scientific basis for effective prevention and control of DTC. The data on DTC cases in Lujiang County, China, were downloaded from the Data Center of the Center for Disease Control and Prevention in Hefei, Anhui Province, China, while the demographic data were sourced from the demographic department in China. Systematic statistical analyses, including the spatial empirical Bayes smoothing, spatial autocorrelation, hotspot statistics, and Kulldorff’s retrospective space-time scan, were used to identify the spatial and spatiotemporal clusters of DTC. GM(1,1) and standard deviation ellipses were then applied to predict the future evolution of the spatial pattern of the DTC cases in Lujiang County. The results showed that DTC in Lujiang County had obvious spatiotemporal clustering. The spatial distribution of DTC cases increases gradually from east to west in the county in a stepwise pattern. The peak of DTC cases occurred in 2012–2013, and the high-case spatial clusters were located mainly in the northwest of Lujiang County. At the 99% confidence interval, two spatiotemporal clusters were identified. From 2012 to 2017, the cases of DTC in Lujiang County gradually shifted to the high-incidence area in the northwest, and the spatial distribution range experienced a process of “dispersion-clustering”. The cases of DTC in Lujiang County will continue to move to the northwest from 2018 to 2025, and the predicted spatial clustering tends to be more obvious.

Environmental pollution has attracted growing government attention. We employ a series of panel data regression models to measure and analyze the impact of environmental attention of 284 prefecture-level municipal governments on ambient pollution in China. The results show that: (1) The improvement of government environmental attention can curb ambient pollution. (2) The impact of government environmental attention on ambient pollution is heterogeneous in the difference of regional and local environmental pollution severity. (3) Government environmental attention inhibits ambient pollution through green development and industrial upgrading. The conclusions of this paper provide evidence and implications for environmental regulation in developing countries and cities.

Human activities involving nature have various environmental impacts. The assessment of the spatial and temporal evolution of human activity intensity (HAI) and its driving forces is significant for determining the effects of human activities on regional ecological environments and regulating such activities. This research quantified the HAI of China, assessed its spatiotemporal characteristics, and analyzed its influencing factors based on the land use data and panel data of 31 provinces in mainland China. The results indicate that the HAI in China is increasing, with the average value increasing from 15.83% in 1980 to 20.04% in 2018, and the HAI was relatively serious in the Beijing–Tianjin–Hebei region, Yangtze River Delta and Pearl River Delta in this period. The spatial differences in the HAI in China show a pattern of being strong in the east and weak in the west, and the spatial center of gravity of China’s HAI has gradually moved west, changing from a central enhancement mode to a point-like “core” enhancement mode. The dominant factors affecting spatial differences in HAI are economic and industrial levels. Labor, population, and capital factors also strongly impact HAI, and energy consumption and pollution emissions have little impact. These results deepen the understanding of the underlying mechanism of the environmental impact of human activities and provide a scientific basis for land-use-related decision making and eco-environment construction.

Stubble burning (SB) has been a major source of seasonal aerosol loading and pollution over northern India. The aftereffects of groundwater preservation act i.e., post 2010 era (2011–2020) has seen delay in crop harvesting thereby shifting the peak SB to May (Wheat SB) and to November (Paddy SB) by 8–10 and 10–12 days compared to pre-2010. Groundwater storage depletion rate of 29.2 mm yr⁻¹ was observed over the region. Post 2010 era shows an increase of 1.4% in wheat SB and 21% in Paddy SB fires over Punjab and Haryana with 70% of PM_2.5 air mass clusters (high probability > 0.8) advecting to the downwind regions leading to 23–26% increase in PM_2.5 and 4–6% in aerosol loading over National Capital Region (NCR). Although the objective of water conservation policy was supposed to preserve the groundwater by delaying the paddy transplantation and sowing, on the contrary the implementation of this policy has seen groundwater storage after 2013 depleting at a rate of 29.2 mmyr⁻¹ over these regions. Post policy implementation has led to shift and shrinking of harvest window with increased occurrences in SB fires which also increase associated particulate matter pollution over North India.

The structure adjustment and layout optimization of water pollution-intensive industries (WPIIs) are crucial to the health and sustainable development of the watershed life community. Based on micro-detailed data of Chinese industrial enterprises from 2003 to 2013, we analyzed and revealed the spatial differentiation characteristics and influencing factors of WPIIs in the Yellow River Basin (YRB) from 2003 to 2013 by constructing a water pollution-intensive index and integrating kernel density estimation and geographically weighted regression models from a watershed perspective. The results show that: (1) the scale of WPIIs in the YRB showed a growth trend from 2003 to 2013, and the output value increased from 442.5 billion yuan in 2003 to 6192.4 billion yuan in 2013, an increase of 13 times. (2) WPIIs are generally distributed in an east-west direction, and their spatial distribution is river-side, with intensive distribution in the downstream areas and important tributaries such as Fen River and Wei River. (3) WPIIs are generally clustered in high density downstream, but the spatial clustering characteristics of different industries varied significantly. The chemical industries, paper industries, etc. were mainly concentrated in downstream areas. Processing of food from agricultural products was distributed in the upper, middle and downstream areas. Resource-intensive industries such as coal and oil were concentrated in energy-rich midstream areas. (4) Natural resource endowment was the main factor affecting the distribution of WPIIs in the midstream and upstream areas of the basin, and technological innovation played a significant role in the distribution of downstream industries. The level of economic development and industrial historical foundation promoted the geographical concentration of industries. The scale of wastewater discharge and the proximity of rivers influenced the concentration of industries in the midstream and downstream.

Flash floods show strong regional differentiation in spatial–temporal distribution and driving forces, thereby hindering their effective prevention and control. This study analyzed the spatiotemporal characteristics of flash floods in Shaanxi Province, China, differentiated among the northern Shaanxi (NS), Guanzhong (GZ), and southern Shaanxi (SS) regions based on the Mann–Kendall, Theil–Sen Median, and standard deviation ellipse methods. The main factors driving disasters and their interactions in each region were then identified within the three categories of precipitation factor (PPF), surface environment factor, and human activity factor (HAF) based on a geographical detector. Finally, the differences in flash flood characteristics among the NS, GZ, and SS regions were analyzed. The results showed that flash floods in Shaanxi Province are greatly affected by the PPF and the HAF, although the spatial–temporal characteristics and disaster-causing factors were significantly different in each region. The regions were ranked according to the number and growth trends of flash floods as follows: SS > GZ > NS. Furthermore, flash floods were affected by multiple factors, with the interaction between factors acting as a driving force of flash floods. The results of this study can provide a reference for the management of flash floods under regional differentiation.

Nitrogen dioxide (NO₂) has a great influence on atmospheric chemistry. Scientifically identifying the temporal-spatial characteristics of NO₂ distribution and their driving factors will be of realistic significance to atmospheric governance in the Yangtze River Economic Belt (YREB). Based on the NO₂ data derived from the TROPOspheric Monitoring Instrument (TROPOMI) onboard the Sentinel-5 satellite (2017~present), spatial autocorrelation analysis, standard deviation ellipse (SDE), and geodetectors were used to systematically analyze the spatial-temporal evolution and driving factors of tropospheric NO₂ vertical column density (NO₂ VCD) in the YREB from 2019 to 2020. The results showed that the NO₂ VCD in the YREB was high in winter and autumn and low in spring and summer (temporal distribution), and high in the northeast and low in the southwest (spatial distribution), with significant spatial agglomeration. High-value agglomeration zones were collectively and stably distributed in the east region, while low-value zones were relatively dispersed. The explanatory power of each potential factor for the NO₂ VCD showed regional and seasonal variations. Surface pressure was found to be a core influencing factor. Synergistic effects of factors presented bivariate enhancement or nonlinear enhancement, and interaction between any two factors strengthened the explanatory power of a single factor for the NO₂ VCD.

Background: We present a systematic review of studies assessing the association between ambient particulate matter (PM) and premature mortality and the results of a Bayesian hierarchical meta-analysis while accounting for population differences of the included studies. Methods: The review protocol was registered in the PROSPERO systematic review registry. Medline, CINAHL and Global Health databases were systematically searched. Bayesian hierarchical meta-analysis was conducted using a non-informative prior to assess whether the regression coefficients differed across observations due to the heterogeneity among studies. Results: We identified 3248 records for title and abstract review, of which 309 underwent full text screening. Thirty-six studies were included, based on the inclusion criteria. Most of the studies were from China (n = 14), India (n = 6) and the USA (n = 3). PM_2.5 was the most frequently reported pollutant. PM was estimated using modelling techniques (22 studies), satellite-based measures (four studies) and direct measurements (ten studies). Mortality data were sourced from country-specific mortality statistics for 17 studies, Global Burden of Disease data for 16 studies, WHO data for two studies and life tables for one study. Sixteen studies were included in the Bayesian hierarchical meta-analysis. The meta-analysis revealed that the annual estimate of premature mortality attributed to PM_2.5 was 253 per 1,000,000 population (95% CI: 90, 643) and 587 per 1,000,000 population (95% CI: 1, 39,746) for PM₁₀. Conclusion: 253 premature deaths per million population are associated with exposure to ambient PM_2.5. We observed an unstable estimate for PM₁₀, most likely due to heterogeneity among the studies. Future research efforts should focus on the effects of ambient PM₁₀ and premature mortality, as well as include populations outside Asia. Key messages: Ambient PM_2.5 is associated with premature mortality. Given that rapid urbanization may increase this burden in the coming decades, our study highlights the urgency of implementing air pollution mitigation strategies to reduce the risk to population and planetary health.

Human health and the environment are adversely affected by fine particulate matter. By utilizing standard deviation ellipse and trend analyses, we studied the spatial patterns and temporal trends of PM_2.5 over Pakistan from 1998 to 2016. The outcomes of these analyses indicated that PM_2.5 concentrations were considerably amplified in Pakistan, particularly in the provinces of Punjab and Sindh. The areal extent of PM_2.5 concentrations below 15 μg/m³ declined constantly, and the area with PM_2.5 concentrations above 35 μg/m³ increased significantly. The highly affected cities were Lahore, Faisalabad, Multan, Southern Gujranwala, Dera Ghazi Khan, Bahawalpur, Sukkur, and Larkana. Overall, the northwest‐southeast axis experienced more rapid variations in the spatial pattern of PM_2.5 than the northeast‐southwest axis; similarly, the east–north axis also experienced faster changes in the spatial distribution of this crucial pollutant than the west–south axis. To support nationwide air pollution control, a 2‐tier level was recommended for allocated regions in Pakistan depending on their PM_2.5 concentrations. From 1998 to 2016, health risks expanded and increased in Pakistan, particularly in Lahore, Karachi, Multan, Gujranwala, Faisalabad, and Hyderabad; these are Pakistan's most populated cities. The outcomes of this study suggest that human health is continuously affected by PM_2.5 in Pakistan and that a plan of action to combat air pollution is immediately needed.

Objective: The purpose of the research was to investigate and identify the impact of COVID-19 lockdown on fine particulate matter (PM_2.5) pollution in Dhaka, Bangladesh by using ground-based observation data.Methods: The research assessed air quality during the COVID-19 pandemic for PM_2.5 from January 1, 2017 to August 1, 2020. The research considered pollution in pre-COVID-19 (January 1 to March 23), during COVID-19 (March 24 to May 30), and post-COVID-19 (May 31 to August 1) lockdown periods with current (2020) and historical (2017-2019) data.Results: PM_2.5 pollution followed a similar yearly trend in year 2017-2020. The average concentration for PM_2.5 was found 87.47 μg/m³ in the study period. Significant PM_2.5 declines were observed in the current COVID-19 lockdown period compared with historical data: 11.31% reduction with an absolute decrease of 7.15 μg/m³.Conclusions: The findings of the research provide an overview of how the COVID-19 pandemic affects air pollution. The results will provide initial evidence regarding human behavioral changes and emission controls. This research will also suggest avenues for further study to link the findings with health outcomes.

In the rapid process of urbanization in China, arable land resources are faced with dual challenges in terms of quantity and quality. Starting with the change in the coupling coordination relationship between the input and output on arable land, this study applies an evaluation model of the degree of coupling coordination between the input and output (D_CCIO) on arable land and deeply analyzes the recessive transition mechanism and internal differences in arable land use modes in 31 provinces on mainland China. The results show that the total amount and the amount per unit area of the input and output on arable land in China have presented different spatio-temporal trends, along with the mismatched movement of the spatial barycenter. Although the D_CCIO on arable land increases slowly as a whole, 31 provinces show different recessive transition mechanisms of arable land use, which is hidden in the internal changes in the input–output structure. The results of this study highlight the different recessive transition patterns of arable land use in different provinces of China, which points to the outlook for higher technical input, optimized planting structure, and the coordination of human-land relationships.

Introduction: Over the past two decades, China has experienced rapid economic development, which has not only led to a rapid increase in the use of raw materials but has also created environmental problems. This research analyzes the environmental impacts of resource extraction in China at the provincial level, and fully considers the environmental impact of various resources extraction. In addition, it is the first time to quantitatively study the spatial pattern and evolution characteristics of the environmental impacts of China’s resource extraction from multiple perspectives by means of spatial visualization. Outcomes: The results showed that the center of gravity of abiotic depletion potential (ADP) moved northwest, respiratory inorganics (RI) moved southwest and global warming potential (GWP) moved west. The results of the standard deviation ellipse showed that RI and GWP varied over time and space, while ADP showed a discrete trend. In addition, the distribution of the four in the northeast-southwest direction became more prominent. Conclusion: To mitigate the environmental impacts of resource extraction, we recommend that regional governments implement measures to control environmental impacts in the provinces within the distributed ellipse and design targeted policies based on actual conditions.

In this study, we integrated a remote-sensing fire product (MOD14A1) and land-use product (MCD12Q1) to extract the number of crop-residue burning (CRB) spots and the fire radiative power (FRP) in China from 2001 to 2018. Moreover, we conducted three trend analyses and two geographic distribution analyses to quantify the interannual variations and summarize the spatial characteristics of CRB on grid (0.25° × 0.25°) and regional scales. The results indicated that CRB presents distinctive seasonal patterns with each sub-region. All trend analyses suggested that the annual number of CRB spots in China increased significantly from 2001 to 2018; the linear trend reached 2615 spots/year, the Theil-Sen slope was slightly lower at 2557 spots/year, and the Mann-Kendal τ was 0.75. By dividing the study period into two sub-periods, we found that the five sub-regions presented different trends in the first and second sub-periods; e.g., the Theil-Sen slope of eastern China in the first sub-period (2001–2009) was 1021 spots/year but was −1599 spots/year in the second period (2010–2018). This suggests that summer CRB has been effectively mitigated in eastern China since 2010. Further, the average FRP of CRB spots presented a decreasing trend from 27.5 MW/spot in 2001 to only 15.8 MW/spot in 2018; this may be attributable to more scattered CRB rather than aggregated CRB. Collectively, the fire spots, FRP, and average FRP indicated that spring, summer, and autumn CRB had dropped dramatically over previous levels by 2018 due to strict mitigation measures by local governments.

Satellite observations show that the rapid urbanization and emergence of megacities with 10 million or more residents have raised PM2.5 concentrations across the globe during the past few decades. This study examines PM2.5 dynamics for the 33 cities included on the UN list of megacities published in 2018. These megacities were classified into densely (>1500 residents per km²), moderately (300–1500 residents per km²) and sparsely (<300 residents per km²) populated areas to examine the effect of human population density on PM2.5 concentrations in these areas during the period 1998–2016. We found that: (1) the higher population density areas experienced higher PM2.5 concentrations; and (2) the megacities with high PM2.5 concentrations in these areas had higher concentrations than those in the moderately and sparsely populated areas of other megacities as well. The numbers of residents experiencing poor air quality is substantial: approximately 452 and 163 million experienced average annual PM2.5 levels exceeding 10 and 35 μg/m³, respectively in 2016. We also examined PM2.5 trends during the past 18 years and predict that high PM2.5 levels will likely continue in many of these megacities in the future without substantial changes in their economies and/or pollution abatement practices. There will be more megacities in the highest PM2.5 pollution class and the number of megacities in the lowest PM2.5 pollution class will likely not change. Finally, we analyzed how the PM2.5 pollution burden varies geographically and ranked the 33 megacities in terms of PM2.5 pollution in 2016. The most polluted regions are China, India, and South Asia and the least polluted regions are Europe and Japan. None of the 33 megacities currently fall in the WHO’s PM2.5 attainment class (35 μg/m³). In 2016, the least polluted megacity was New York and most polluted megacity was Delhi whose average annual PM2.5 concentration of 110 μg/m³ is nearly three times the WHO’s non-attainment threshold.

The Republic of the Union of Myanmar is one of many developing countries facing concerns about particulate matter (PM). Previously, a preliminary study of PM2.5 in 2018 suggested that the main source of PM in Yangon, the former capital, was vehicle emissions. However, this suggestion was not supported by any chemical composition data. In this study, to fill that gap, we quantitatively determined source contributions to coarse particulate matter (PM10) in Yangon, Myanmar. PM10 samples were collected in Yangon from May 2017 to April 2018 and chemically analyzed to determine composition. Chemical composition data for these samples were then used in the Chemical Mass Balance (CMB) model to identify the major sources of particulate matter in this area. The results indicate that PM10 composition varies seasonally according to both meteorological factors (e.g., precipitation and temperature) and human activities (e.g., firewood and yard waste burning). The major sources of PM in Yangon annually were dust, secondary inorganic aerosols (SIA), and secondary organic aerosols (SOA), while contributions from biomass burning were more important during the winter months.

Flash floods are one of the most destructive natural disasters. The comprehensive identification of the spatiotemporal characteristics and driving factors of a flash flood is the basis for the scientific understanding of the formation mechanism and the distribution characteristics of flash floods. In this study, we explored the spatiotemporal patterns of flash floods in Fujian Province from 1951 to 2015. Then, we analyzed the driving forces of flash floods in geomorphic regions with three different grades based on three methods, namely, geographical detector, principal component analysis, and multiple linear regression. Finally, the sensitivity of flash floods to the gross domestic product, village point density, annual maximum one-day precipitation (Rx1day), and annual total precipitation from days > 95th percentile (R95p) was analyzed. The analytical results indicated that (1) The counts of flash floods rose sharply from 1988, and the spatial distribution of flash floods mainly extended from the coastal low mountains, hills, and plain regions of Fujian (IIA2) to the low-middle mountains, hills, and valley regions in the Wuyi mountains (IIA4) from 1951 to 2015. (2) From IIA2 to IIA4, the impact of human activities on flash floods was gradually weakened, while the contribution of precipitation indicators gradually strengthened. (3) The sensitivity analysis results revealed that the hazard factors of flash floods in different periods and regions had significant differences in Fujian Province. Based on the above results, it is necessary to accurately forecast extreme precipitation and improve the economic development model of the IIA2 region.

The extensive uses of the air filters have encouraged the advancement of air filter technology on both fabrication and characterization. Due to the high demand to those filters, the quality assessment to the filters are massively needed with a lower test cost and accessible. We developed a personal air filter test (PAFT) system for measuring the filter pressure drop, efficiency, and quality factor. The PAFT system utilized a PM sensor (Sharp, GP2Y1010AU0F) for measuring the filtration efficiency. Accordingly, performance evaluation and optimization to the PM sensor were done to guarantee their compatibility for this application. The performance evaluation studied the sensor output responses to sampling flow, particle diameter, and PM sources. We also improved the sensor sensitivity. The experimental results show that the sensor has no significant influences on the sampling flow. The sensor output was highly dependent on the particle size and PM source, but their response curves remained linear, which was an advantage for filter efficiency measurement. We measured the efficiency of nanofiber filters having various efficiencies, and comparing the results to reference efficiency as measured by a CPC (TSI, 3772). The test resulted in a filtration coefficient (K_f), which was used to correct the PAFT efficiency measurement results. We also conducted the test to some commercial mask filters, and the filtration efficiency measurement results showed a good agreement to the reference with a small average error of about 2.5%. The complete design of the PAFT and experimental methods will be discussed in detail.

Over the last 4 decades, Asian countries have undergone substantial economic development, leading to rapid urbanization and industrialization. Consequently, fossil fuel consumption has risen dramatically, worsening the air quality in Asia. Fossil fuel combustion emits particulate matter containing toxic metals that can adversely affect living organisms, including humans. Thus, it is imperative to investigate the temporal and spatial extent of metal pollution in Asia. Recently, we reported a continuous and high-resolution 1650–1991 ice core record from the Guliya ice cap in northwestern Tibet, China, showing contamination of Cd, Pb, and Zn during the 20th century. Here, we present a new continuous and high-resolution ice core record of trace metals from the Guliya ice cap that comprises the years between 1971 and 2015, extending the 1650–1991 ice core record into the 21st century. Non-crustal Cd, Pb, Zn, and Ni enrichments increased have since the 1990s relative to the 1971–1990 period, reaching a maximum in 2008. The enrichments of Cd, Pb, Zn, and Ni increased by ∼75 %, 35 %, 30 %, and 10 %, respectively, during the 2000–2015 period relative to 1971–1990. The observed trace element (TE) enrichments likely originated primarily from fossil fuel combustion and biomass burning, with contributions from industrial processes and agricultural activities from South Asia (Pakistan, Afghanistan, India, and Nepal), Central Asia (Kazakhstan, Kyrgyzstan, Tajikistan, Turkmenistan, and Uzbekistan), and the Xinjiang province in western China. This new record demonstrates that the current emissions in Asia impact remote high-altitude glaciers in the region.