[Evolution of Physical and Chemical Characteristics of Atmospheric Precipitation and Its Significant Impacts on the Environment in Beijing].

Atmospheric precipitation samples were collected in 2018, 2019, and 2021 in Beijing to study the concentrations and changes of the main metal elements and water-soluble ions； the wet deposition fluxes of heavy metals, water-soluble ions, dissolved inorganic nitrogen, and sulfur in the atmospheric precipitation and their impacts on the ecological environment； and the scavenging mechanisms of the typical precipitation to atmospheric pollutants during the study period. The results showed that the precipitation in Beijing during the study period was mostly neutral or alkaline, and the frequency of acid rain occurrence was very low, only accounting for 3.06%. The total concentrations of major metal elements in 2018, 2019, and 2021 were （4 787.46 ±4 704.31）, （7 663.07 ±8 395.05）, and （2 629.13 ±2 369.51） µg·L-1, respectively. The total equivalent concentrations of ions in 2018, 2019, and 2021 were （851.68 ±649.16）, （973.98 ±850.94）, and （644.31 ±531.16） µeq·L-1, respectively. The interannual changes in major metal elements and ions followed the order of 2019 > 2018 > 2021. The seasonal average total concentrations of major metal elements in spring, summer, autumn, and winter were （9 624.25 ±7 327.92）, （4 088.67 ±5 710.14）, （3 357.68 ±3 995.64）, and （6 203.19 ±3 857.43） µg·L-1, respectively, and the seasonal average total equivalent concentrations of ions in spring, summer, autumn, and winter were （1 014.71 ±512.21）, （729.83 ±589.90）, （724.35 ±681.40）, and （1 014.03 ±359.67） µeq·L-1, respectively, all presenting the order of spring > winter > summer > autumn. NO3- and SO42- were the main acid-causing ions in precipitation, whereas NH4+ and Ca2+ were the main acid-neutralizing ions. The wet deposition fluxes of the heavy metal Cd were very low [（0.05 ±0.01） mg·ï¼ˆm2·a）-1], only accounting for （0.13 ±0.04）% of the total wet deposition fluxes of main metal elements； however, its soil safety years were 291 years, significantly lower than those of other heavy metals, displaying that its ecological risk was relatively the highest. The total wet precipitation flux of water-soluble ions NH4+, Ca2+, NO3-, and SO42- accounted for （85.72 ±2.18）% of the wet precipitation flux of total ions, suggesting that their comprehensive impact on the ecological environment might have been higher. DIN wet deposition flux was mainly characterized by NH4+-N, which had a positive impact on the ecological environment in summer. SO42--S wet deposition flux was higher in summer, so its positive impact on the ecological environment was also greater. The scavenging effects of atmospheric precipitations to pollutants from the air were impacted by various factors, and the synergism effects of these factors could directly influence the scavenging mechanisms of precipitation to pollutants.

eDNA-based survey of the marine vertebrate biodiversity off the west coast of Guadeloupe (French West Indies).

Background: In the marine environment, knowledge of biodiversity remains incomplete for many taxa, requiring assessments to understand and monitor biodiversity loss. Environmental DNA (eDNA) metabarcoding is a powerful tool for monitoring marine biodiversity, as it enables several taxa to be characterised simultaneously in a single sample. However, the data generated by environmental DNA metabarcoding are often not easily reusable. Implementing FAIR principles and standards for eDNA-derived data can facilitate data-sharing within the scientific community. New information: This study focuses on the detection of marine vertebrate biodiversity using eDNA metabarcoding on the leeward coast of Guadeloupe, a known hotspot for marine biodiversity in the French West Indies. Occurrences and DNA-derived data are shared here using DarwinCore standards combined with MIMARKS standards.

Temporal variations of bacterial and eukaryotic community in coastal waters-implications for aquaculture.

Despite increased attention to the aquaculture environment, there is still a lack of understanding regarding the significance of water quality. To address this knowledge gap, this study utilized high-throughput sequencing of 16S rRNA and 18S rRNA to examine microbial communities (bacteria and eukaryotes) in coastal water over different months through long-term observations. The goal was to explore interaction patterns in the microbial community and identify potential pathogenic bacteria and red tide organisms. The results revealed significant differences in composition, diversity, and richness of bacterial and eukaryotic operational taxonomic units (OTUs) across various months. Principal coordinate analysis (PCoA) demonstrated distinct temporal variations in bacterial and eukaryotic communities, with significant differences (P = 0.001) among four groups: F (January-April), M (May), S (June-September), and T (October-December). Moreover, a strong association was observed between microbial communities and months, with most OTUs showing a distinct temporal preference. The Kruskal-Wallis test (P < 0.05) indicated significant differences in dominant bacterial and eukaryotic taxa among months, with each group exhibiting unique dominant taxa, including potential pathogenic bacteria and red tide organisms. These findings emphasize the importance of monitoring changes in potentially harmful microorganisms in aquaculture. Network analysis highlighted positive correlations between bacteria and eukaryotes, with bacteria playing a key role in network interactions. The key bacterial genera associated with other microorganisms varied significantly (P < 0.05) across different groups. In summary, this study deepens the understanding of aquaculture water quality and offers valuable insights for maintaining healthy aquaculture practices. KEY POINTS: • Bacterial and eukaryotic communities displayed distinct temporal variations. • Different months exhibited unique potential pathogenic bacteria and red tide organisms. • Bacteria are key taxonomic taxa involved in microbial network interactions.

[Landscape Pattern Vulnerability and Its Driving Forces in Different Geomorphological Divisions in the Middle Yellow River].

The ecological environment of the middle Yellow River is highly vulnerable. Conducting a scientific assessment of landscape pattern vulnerability holds great significance, as it serves as the basis for the rational construction of the ecological environment in this area. Based on five periods of land use data from the middle Yellow River from 1990 to 2018, the landscape pattern vulnerability index was employed to analyze the spatio-temporal evolution of the landscape pattern vulnerability. Furthermore, the influencing factors for landscape pattern vulnerability in different natural geomorphological divisions were explored using an optimal parameters-based geographical detector model. The results showed that:① From 1990 to 2018, cultivated land (which accounted for 36.96 % to 39.97 % of the area) remained the predominant landscape in the middle Yellow River. Among all landscape types, cultivated land and construction land exhibited the most significant changes. The area of cultivated land decreased by 10 185.00 km2, whereas the area of construction land increased by 7 678.46 km2. ② From 1990 to 2018, the landscape pattern was dominated by low and medium vulnerability and accounted for 70 %-80 % of the total area. The high and higher vulnerability areas were concentrated in the loess hilly and gully region, whereas the lower vulnerability area was concentrated in the valley plain and the earth-rock mountain regions. During this period, landscape pattern vulnerability underwent an incipient decrease, followed by a subsequent increase. From 1990 to 2000 and from 2000 to 2005, the changes in the level of landscape pattern vulnerability were dominated by a "reduction in the degree of vulnerability". However, from 2005 to 2010 and from 2010 to 2018, it was mainly an "increase in the degree of vulnerability". ③ Annual precipitation and NDVI were the main factors influencing the vulnerability of landscape patterns, whereas the influencing factors varied across different natural geomorphological divisions:the loess hilly and gully region and the earth-rock mountain region were dominated by natural factors, with annual precipitation and DEM being the dominant factors, respectively; the loess plateau tableland-gully region, valley plain region, and sandy land and desert region were dominated by human factors, with population density, degree of land use, and distance from roads being the dominant factors, respectively. The interaction results of any two influencing factors were manifested as two-factor enhancement or nonlinear enhancement. Risk detection revealed that high vulnerability areas of landscape patterns in different natural geomorphological divisions were distributed over distinct ranges of their corresponding dominant factors. Therefore, in the practices of ecological management in the middle Yellow River, appropriate management strategies should be implemented based on the vulnerability characteristics of different natural landforms, to further improve the ecological management level of the watershed.

Water quality assessment in a large plateau lake in China from 2014 to 2021 with machine learning models: Implications for future water quality management.

Amid the global surge of eutrophication in lakes, investigating and analyzing water quality and trends of lakes becomes imperative for formulating effective lake management policies. Water quality index (WQI) is one of the most used tools to assess water quality by integrating data from multiple water quality parameters. In this study, we analyzed the spatio-temporal variations of 11 water quality parameters in one of the largest plateau lakes, Erhai Lake, based on surveys from January 2014 to December 2021. Leveraging machine learning models, we gauged the relative importance of different water quality parameters to the WQI and further utilized stepwise multiple linear regression to derive an optimal minimal water quality index (WQImin) that required the minimal number of water quality parameters without compromising the performance. Our results indicated that the water quality of Erhai Lake typically showed a trend towards improvement, as indicated by the positive Mann-Kendall test for WQI performance (Z = 2.89, p < 0.01). Among the five machine learning models, XGBoost emerged as the best performer (coefficient of determination R2 = 0.822, mean squared error = 3.430, and mean absolute error = 1.460). Among the 11 water quality parameters, only four (i.e., dissolved oxygen, ammonia nitrogen, total phosphorus, and total nitrogen) were needed for the optimal WQImin. The establishment of the WQImin helps reduce cost in future water quality monitoring in Erhai Lake, which may also serve as a valuable framework for efficient water quality monitoring in similar waters. In addition, the elucidation of spatio-temporal patterns and trends of Erhai Lake's water quality serves as a compass for authorities, offering insights to bolster lake management strategies in the future.

Spatio-temporal distribution of sea surface chlorophyll-a in coral reefs of the South China Sea over the past decade based on Landsat-8 Operational Land Images.

The concentration of chlorophyll-a (Chl-a) in seawater reflects phytoplankton growth and water eutrophication, which are usually assessed for evaluation of primary productivity and carbon source/sink of coral reefs. However, the precise delineation of Chl-a concentration in coral reefs remains a challenge when ocean satellites with low spatial resolution are utilized. In this study, a remote sensing inversion model for Chl-a was developed in fringing reefs (R2 = 0.76, RMSE =0.41 µg/L, MRE = 14 %) and atolls (R2 = 0.79, RMSE =0.02 µg/L, MRE = 8 %), utilizing reflectance data from the sensitive band of the Landsat-8 Operational Land Imagers (OLI) with a spatial resolution of 30 m. The aforementioned model was utilized to invert high-resolution distribution maps of Chl-a concentration in six major coral reef regions of the South China Sea from 2013 to 2022 and subsequently used to analyze the variations in Chl-a concentration and its influencing factors. The results indicate a Chl-a concentration gradient among coral reefs Daya Bay, Weizhou Island, Luhuitou, Xuwen, Huangyan Island, and Xisha Island in that order. The Chl-a concentration in coral reefs exhibited an overall increasing trend, with significant seasonal fluctuations, characterized by higher concentrations during winter and spring and lower concentrations during summer and autumn. The concentration of Chl-a in coral reefs was positively correlated with the average wind speed.

[Critical Review on Heavy Metal Contamination in Urban Soil and Surface Dust].

With the rapid urbanization and industrialization, heavy metal contamination in urban soil and surface dust has received particular attention due to its negative effects on the eco-environment and human health. Contamination and spatio-temporal characteristics, contamination sources, and source apportionment methods, as well as the ecological and health risks of heavy metals in urban soil and surface dust were reviewed. The knowledge gaps in current research and prospects of future works were proposed. Four key points were presented, including improving the research on the interaction mechanism of heavy metals in urban soil and surface dust under complex conditions, enriching verification methods to improve the source apportionment reliability of anthropogenic metals by receptor models, strengthening the research on chemical forms of heavy metals from different sources and their short-term accumulation processes in surface dust, and raising the credibility of ecological and health risk forecast of heavy metals by integrating the improved exposure parameters and chemical forms.

Fine-Scale Spatial Variability of Greenhouse Gas Emissions From a Subantarctic Peatland Bog.

Peatlands are recognized as crucial greenhouse gas sources and sinks and have been extensively studied. Their emissions exhibit high spatial heterogeneity when measured on site using flux chambers. However, the mechanism by which this spatial variability behaves on a very fine scale remains unclear. This study investigates the fine-scale spatial variability of greenhouse gas emissions from a subantarctic Sphagnum peatland bog. Using a recently developed skirt chamber, methane emissions and ecosystem respiration (as carbon dioxide) were measured at a submeter scale resolution, at five specific 3 × 3 m plots, which were examined across the site throughout a single campaign during the Austral summer season. The results indicated that methane fluxes were significantly less homogeneously distributed compared with ecosystem respiration. Furthermore, we established that the spatial variation scale, i.e., the minimum spatial domain over which notable changes in methane emissions and ecosystem respiration occur, was <0.56 m2. Factors such as ground height relative to the water table and vegetation coverage were analyzed. It was observed that Tetroncium magellanicum exhibited a notable correlation with higher methane fluxes, likely because of the aerenchymatous nature of this species, facilitating gas transport. This study advances understanding of gas exchange patterns in peatlands but also emphasizes the need for further efforts for characterizing spatial dynamics at a very fine scale for precise greenhouse gas budget assessment.

Prediction of heavy metal lead contamination accident in Three Gorges Reservoir Area.

Water contamination incidents have become a significant ecological and environmental threat, particularly concerning the security of drinking water source areas (DWSAs). This research aimed to address this issue by integrating Geographic Information System (GIS) into bidimensional hydrodynamic water quality mathematical model developed using C + + and FORTRAN programming languages. The focus was on the Heshangshan drinking water source area (HDWSA), and the TECPLOT360 software was utilized for visualizing pollutant migration and dispersion processes. The study specifically considered a hypothetical lead (Pb) contamination accident, which is situated in the Three Gorges Reservoir Area (TGRA). The spatio-temporal variations in Pb concentration throughout the entire DWSA were analyzed, along with a comparison of Pb concentration changes during different water seasons. The results indicate that, during the accident, the Pb concentration at the water intake in the drought season, decline season, flood season, and impounding season reached the standard limits at 76, 58, 44, and 48 min, respectively. Moreover, the entire DWSA achieved standard levels of Pb concentration at 124, 89, 71, and 74 min during the respective seasons. The study also observed an expansion and subsequent contraction of the Pb contamination area in the DWSA, with the transfer rate of Pb concentration ranked as flood season > impounding season > decline season > drought season.

Traffic Noise Assessment in Urban Puducherry, South India.

Background Noise pollution is an emerging global problem that can affect people's well-being and mental and physical health. In India, six percent of people suffer hearing loss, and prolonged exposure leads to irreversible noise-induced hearing loss. Objective To assess the noise levels at selected residential, commercial, industrial, silence zones, traffic junctions, and related noise indices in urban Puducherry and compare them with Central Pollution Control Board (CPCB) standards. Methods The study was conducted using a cross-sectional noise survey based on the 2015 study sites in urban Puducherry using a sound level meter, analyzed the results with limits set by the CPCB standards, and calculated the various noise indices. Results In urban Puducherry, the noise level showing silence zones is more hazardous than industrial, residential, commercial, and traffic junctions. Out of the 36 sites surveyed, 33 locations are above the prescribed daytime limit by CPCB. Conclusions The noise assessment at selected sites in urban Puducherry shows that around 92% of study sites are well above the daytime standards of CPCB, highlighting an urgent need to curb noise levels. The findings revealed that increased noise at study sites could be due to the increased number of vehicles and transportation systems.

Influence of industrial sustainability transition on air quality in a typical resource-exhausted city.

The industrial transition of resource-exhausted cities is the focus of attention, and air quality has naturally become an important indicator to measure the sustainable development quality. Aerosol optical depth (AOD) is an important parameter to indicate air quality. This paper aimed to study the influence of industrial transition on air quality and provide a list of recommendations and management strategies for sustainable development in resource-exhausted cities. Results showed the secondary industry played important roles for economic development before 2015, however, it decreased after 2014, and the tertiary industry played more and more important roles from 2015. Analyses of the spatial distribution of AOD in each year showed that AOD was relatively higher in urban areas with concentrated population, and the threshold range of AOD value with high area ratio in spatial distribution decreased gradually, which was consistent with the analysis results of time series. Results of correlation analyses indicated that air temperature and land surface temperature were the main natural meteorological factors influencing AOD. Gross population, SO2 emission and the cultivated land area were the main socio-economic factors influencing AOD. It could be concluded that the industrial transition of the city has achieved good results, the economic structure has been gradually optimized and adjusted, and the air quality has gradually improved over industrial sustainability transition. Scientific exploitation, energy conservation, application of clean energy and industrial structure optimization would be effective measures for sustainable development.

Temporal Variations in Plankton Communities and Environmental Factors in the Shipra, a Central Himalayan Tributary of the Kosi River in Uttarakhand, India.

The essential role of smaller streams and tributaries indigenous to the area is crucial in revitalizing and restoring the main river system. The present study unraveled the relationship between plankton communities and environmental variables in the Shipra River, a Central Himalayan tributary of the Kosi River in Uttarakhand, India. Plankton distribution, abundance, and presence were investigated at four locations: upstream (S1 and S2) and downstream (S3 and S4). Forty-eight genera of phytoplankton and zooplankton have been identified belonging to ten classes (Bacillariophyceae, Chlorophyceae, Zygnematophyceae, Cyanophyceae, Euglenopyceae, Protozoa, Rotifers, Copepoda, Cladocera, and Insecta) based on the findings. Winter was dominated by phytoplankton (Navicula spp., Nitzchia spp., Diatom spp.); summer and monsoon by zooplankton (Daphnia spp., Cyclops spp., Keratella spp., Brachionus spp.). Plankton communities of the tributary were assessed using diversity indices (Shannon-Weiner's index (H), Simpson's diversity index (1-D), and Evenness Index (E)). Seasonal variations in water temperature, specific conductivity, total dissolved solids, dissolved oxygen, nitrate, and ammonium ions were found to be statistically significant (p<0.05). Canonical Correspondence Analysis, K-dominance plots, cluster analysis, and NMDS analysis showed the dynamic nature of the plankton communities with seasonal physiochemical variations in the unexplored Himalayan tributary. The plankton communities exhibited significant temporal fluctuations with physicochemical factors.

Inter-event and intra-event dynamics of microplastic emissions in an urban river during rainfall episodes.

Urban rivers represent the major conduits for land-sourced microplastics in the global oceans, yet the real-time dynamics of their emissions in rivers during rainfall (and runoff) events are poorly understood. Herein, we report the results of high-frequency sampling of microplastic particles (MPs) and fibers (MPFs) in the surface water of an urban river in Japan over the course of three rainfall events (i.e., light, moderate, and heavy rainfalls). The event mean concentrations (EMCs) of MPs amounted to 35,000 items/m3, 929,000 items/m3, and 331,000 items/m3; and the corresponding total loads were 0.5 kg, 19.8 kg, and 35.0 kg for light, moderate and heavy rainfalls, respectively. The inter-event total loads of MPs correlate well with the total rainfall, while the concentrations were linked with the number of antecedent dry days. The dynamic trends show that <2000 µm MPs displayed first flush effects during light to moderate rainfall events (>50% mass discharged with the initial 20-40% of flow). Small-sized MPs (10-40 µm) mobilized rapidly at lower rainfall intensities, whereas MPs over 2000 µm discharged immediately after the peak rainfall intensity. Moreover, <70 µm MPs depicted a surge following heavy rainfall events due to turbulent flow conditions reverting the deposited MPs into suspension. Overall, the three events increased the loads by 4-110 folds, and EMCs by 10-350 folds compared to the concentrations during dry weather while portraying a significant impact on 300-1000 µm MPs. The dynamics of MPs were correlated with those of suspended solids in river water, and the characteristics were comparable to the same of road dust sampled in Japan. Although the dynamic trends between MPs and MPFs in river water were comparable, MPFs were relatively less impacted by rain, likely due to the intervention of separate sewer systems in the study area.

Temporal variations of antimicrobial resistance genes in aerosols: A one-year monitoring at the puy de Dôme summit (Central France).

The recent characterization of antibiotic resistance genes (ARGs) in clouds evidenced that the atmosphere actively partakes in the global spreading of antibiotic resistance worldwide. Indeed, the outdoor atmosphere continuously receives large quantities of particles of biological origins, emitted from both anthropogenic or natural sources at the near Earth's surface. Nonetheless, our understanding of the composition of the atmospheric resistome, especially at mid-altitude (i.e. above 1000 m a.s.l.), remains largely limited. The atmosphere is vast and highly dynamic, so that the diversity and abundance of ARGs are expected to fluctuate both spatially and temporally. In this work, the abundance and diversity of ARGs were assessed in atmospheric aerosol samples collected weekly between July 2016 and August 2017 at the mountain site of puy de Dôme (1465 m a.s.l., central France). Our results evidence the presence of 33 different subtypes of ARGs in atmospheric aerosols, out of 34 assessed, whose total concentration fluctuated seasonally from 59 to 1.1 × 105 copies m-3 of air. These were heavily dominated by genes from the quinolone resistance family, notably the qepA gene encoding efflux pump mechanisms, which represented >95 % of total ARGs concentration. Its abundance positively correlated with that of bacteria affiliated with the genera Kineococcus, Neorhizobium, Devosia or Massilia, ubiquitous in soils. This, along with the high abundance of Sphingomonas species, points toward a large contribution of natural sources to the airborne ARGs. Nonetheless, the increased contribution of macrolide resistance (notably the erm35 gene) during winter suggests a sporadic diffusion of ARGs from human activities. Our observations depict the atmosphere as an important vector of ARGs from terrestrial sources. Therefore, monitoring ARGs in airborne microorganisms appears necessary to fully understand the dynamics of antimicrobial resistances in the environment and mitigate the threats they may represent.

[Spatial and Temporal Variations in PM1 Concentration and its Correlation with AOD in China from 2014 to 2017].

Based on the PM1 mass concentration data from all the air quality monitoring stations in China from 2014 to 2017, the temporal and spatial distribution characteristics of PM1 concentration were studied using the time series statistical and spatial hierarchical clustering methods, and the PM1 spatiotemporal evolution characteristics were revealed. Combined with AOD data of the MODIS remote-sensing satellite, the temporal and spatial variation in PM1-AOD correlation was analyzed on a fine scale. The results showed that, from 2014 to 2017, the annual average PM1 concentration in China decreased yearly, the seasonal PM1 concentration showed the characteristics of "high in winter and low in summer," and the monthly average PM1 concentration showed a "U"-shaped variation. An "M"-shaped PM1 variation pattern was presented before and after the holidays. Weekly variation showed that high PM1 values occurred on Mondays and Fridays, and low ones occurred on Sundays. Based on the spatial clustering method, the national average annual PM1 concentration in China was divided into seven categories, and the overall spatial distribution pattern was "high in the east and low in the west and high in the north and low in the south." The highest and the lowest values of average PM1 concentration occurred in central China(54.59 µg·m-3) and in Xinjiang-Qinghai-Xizang(11.37 µg·m-3), respectively. The PM1-AOD relationship was positively correlated as a whole, the highest correlation coefficient was 0.55 in central China, and the lowest value was 0.36 in central and southern China.

[Runoff Simulation and Its Response to Extreme Precipitation in the Yangtze River Basin].

Studies on runoff are crucial for the scientific allocation, utilization, and development of water resources and can provide an important basis for the prevention and control of flood and drought disaster, as well as water environmental pollution management. Affected by global warming, the frequency and intensity of extreme climate events, particularly extreme precipitation, have significantly changed in recent years, which can directly or indirectly impact runoff changes. In this study, we used the SWAT model to simulate the spatiotemporal variations in runoff in the Yangtze River Basin from 1965 to 2019 and analyzed the response of runoff to precipitation under extreme conditions. The results showed that the changes in total runoff in the Yangtze River Basin were not significantly different from 1965 to 2019. The total runoff and the mid-lower runoff in the basin experienced four stages of "dry-wet-dry-wet." Simulations revealed that under the 50-year extreme precipitation event, the increase in daily average runoff was 6200%, 21%, and 15% for the typical sub-basins of the upper, middle, and lower reaches of the Yangtze River, respectively. Additionally, the increase in monthly and annual average runoff was 355%, 5%, and 1.3% and 78%, 1%, and 0.24%, for upper, middle, and lower reaches of the Yangtze River, respectively. Moreover, under the 100-year extreme precipitation, the average daily runoff increasing rates were 8000%, 25%, and 17% for upper, middle, and lower reaches of the Yangtze River, respectively, compared to the monthly increase of 437%, 7%, and 1.5% and annual increase of 96%, 1.2%, and 0.28%, respectively. Our findings may improve the understanding of hydrological responses to climate change and provide valuable inferences to decision-makers and water managers for better allocation and management of water resources.

Gaseous elemental mercury emissions from informal E-Waste recycling facilities in Pakistan.

Detrimental effects of mercury (Hg) on ecosystems and human health have been well-documented. Whereas emissions of gaseous elemental mercury (GEM) from e-waste recycling have been reported in developed countries, much less is known about the situation in the Global South. Using a total of 132 passive air samplers, seasonally resolved concentrations of GEM in air were measured continuously at 32 informal e-waste recycling facilities and background location in Pakistan for a period of one year between September 2020 and December 2021. Annual average GEM concentrations at the studied locations ranged from 1.8 to 92 ng m-3. Among the studied cities, higher concentrations were measured in Karachi (mean ± s.d: 17 ± 22, range: 4.2-92 ng m-3), Lahore (16 ± 4.2, 8.2-22 ng m-3) and Peshawar (15 ± 17, 4.9-80 ng m-3), while lower levels were measured in Hyderabad (6.9 ± 6.2, 3.1-25 ng m-3), consistent with a higher rate of informal recycling activities in metropolitan areas. Seasonally, higher GEM levels occurred during autumn (15 ± 16: 3.3-92 ng m-3) and summer (13 ± 8.7: 1.8-80 ng m-3) than in winter (12 ± 8.4: 2.5-49 ng m-3) and spring (9.2 ± 7.3: 1.8-80 ng m-3), possibly reflecting enhanced volatilization at higher temperatures and/or varying magnitude of recycling operations in different seasons. Policies and strict regulations related to e-waste management should be developed and implemented urgently in the country.

Vulnerability assessment of urban agglomerations to the risk of heat waves in China since the 21st century.

In the context of global warming, frequent heat wave disasters have seriously threatened the safety of human life and property. The urban agglomeration, as the main region with a high concentration of population and economy, is susceptible to heat weaves due to the existing urban heat island effect. In this study, we investigated the temporal and spatial characteristics of heat waves (heat index, HI) in China from 2000 to 2020 and assess the vulnerability of 19 urban agglomerations to heat waves from the perspective of exposure, sensitivity and adaptability. The results show that: (1) In the past 20 years, the frequency and intensity of HI (greater than 26.67 °C) both showed an upward trend. (2) Shandong Peninsula, Central Henan, Yangtze River Delta, Middle Reaches of Yangtze River, and Mid-southern Liaoning urban agglomerations always maintain a high vulnerability. (3) From 2000 to 2020, the vulnerability of Beijing-Tianjin-Hebei, Yangtze River Delta, Chengdu-Chongqing, Middle reaches of Yangtze River, Guangdong-Fujian-Zhejiang, Harbin-Changchun and Mid-southern Liaoning urban agglomerations were always dominated by exposure. The vulnerability of Shandong Peninsula, Beibu Gulf and Central Henan urban agglomeration has always been dominated by sensitivity. The vulnerability of North Tianshan Mountain, Lanzhou-Xining, Guanzhong and Hu-Bao-E-Yu urban agglomeration has always been dominated by inadequate adaptability. (4) Recently, the factors that contributed most to exposure, sensitivity and adaptability were population density, the proportion of outdoor workers and water supply, with contribution rates of 38%, 55% and 26%, respectively. This study can provide a scientific basis for the rational allocation of resources among urban agglomerations, effectively formulating policies and guiding population migration from high temperature disasters.

Reconstructing annual XCO2 at a 1 km×1 km spatial resolution across China from 2012 to 2019 based on a spatial CatBoost method.

Long-time-series, high-resolution datasets of the column-averaged dry-air mole fraction of carbon dioxide (XCO2) have great practical importance for mitigating the greenhouse effect, assessing carbon emissions and implementing a low-carbon cycle. However, the mainstream XCO2 datasets obtained from satellite observations have coarse spatial resolutions and are inadequate for supporting research applications with different precision requirements. Here, we developed a new spatial machine learning model by fusing spatial information with CatBoost, called SCatBoost, to fill the above gap based on existing global land-mapped 1° XCO2 data (GLM-XCO2). The 1-km-spatial-resolution dataset containing XCO2 values in China from 2012 to 2019 reconstructed by SCatBoost has stronger and more stable predictive power (confirmed with a cross-validation (R2 = 0.88 and RSME = 0.20 ppm)) than other traditional models. According to the estimated dataset, the overall national XCO2 showed an increasing trend, with the annual mean concentration rising from 392.65 ppm to 410.36 ppm. In addition, the spatial distribution of XCO2 concentrations in China reflects significantly higher concentrations in the eastern coastal areas than in the western inland areas. The contributions of this study can be summarized as follows: (1) It proposes SCatBoost, integrating the advantages of machine learning methods and spatial characteristics with a high prediction accuracy; (2) It presents a dataset of fine-scale and high resolution XCO2 over China from 2012 to 2019 by the model of SCatBoost; (3) Based on the generated data, we identify the spatiotemporal trends of XCO2 in the scale of nation and city agglomeration. These long-term and high resolution XCO2 data help understand the spatiotemporal variations in XCO2, thereby improving policy decisions and planning about carbon reduction.

Temporal and spatial relationships of CrAssphage and enteric viral and bacterial pathogens in wastewater in North Carolina.

Enteric disease remains one of the most common concerns for public health, particularly when it results from human exposure to surface and recreational waters contaminated with wastewater. Characterizing the temporal and spatial variation of enteric pathogens prevalent in wastewater is critical to develop approaches to mitigate their distribution in the environment. In this study, we aim to characterize pathogen variability and test the applicability of the human-associated wastewater indicator crAssphage as an indicator of enteric viral and bacterial pathogens. We conducted weekly samplings for 14 months from four wastewater treatment plants in North Carolina, USA. Untreated wastewater samples were processed using hollow fiber ultrafiltration, followed by secondary concentration methods. Adenovirus, norovirus, enterovirus, Salmonella, Shiga toxin 2 (stx2), Campylobacter, and crAssphage were measured by quantitative polymerase chain reaction (qPCR) and reverse transcriptase (rt)-qPCR. Our results revealed significant correlations between crAssphage and human adenovirus, enterovirus, norovirus, Salmonella, and Campylobacter (p<0.01). Pathogens and crAssphage concentrations in untreated wastewater showed distinct seasonal patterns, with peak concentrations of crAssphage and viral pathogens in fall and winter, while bacterial pathogens showed peaked concentrations in either winter (Campylobacter), fall (Salmonella), or summer (stx2). This study enhances the understanding of crAssphage as an alternative molecular indicator for both bacterial and viral pathogens. The findings of this study can also inform microbial modeling efforts for the prediction of the impact of wastewater pathogens on surface waters due to increased flooding events and wastewater overflows associated with climate change.