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.

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.

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.

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.

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.

The spatiotemporal characteristics, relationship with meteorological factors, and source distribution of air pollutants (January 2017-December 2021) were analyzed to better understand the air pollutants on the northern slope of the Tianshan Mountains (NSTM) in Xinjiang, a heavily polluted urban agglomeration of heavy industries. The results showed that the annual mean concentrations of SO2, NO2, CO, O3, PM2.5, and PM10 were 8.61-13.76 μg m-3, 26.53-36.06 μg m-3, 0.79-1.31 mg m-3, 82.24-87.62 μg m-3, 37.98-51.10 μg m-3, and 84.15-97.47 μg m-3. The concentrations of air pollutants (except O3) showed a decreasing trend. The highest concentrations were in winter, and in Wujiaqu, Shihezi, Changji, Urumqi, and Turpan, the concentrations of particulate matter exceeded the NAAQS Grade II during winter. The west wind and the spread of local pollutants both substantially impacted the high concentrations. According to the analysis of the backward trajectory in winter, the air masses were mainly from eastern Kazakhstan and local emission sources, and PM10 in the airflow had a more significant impact on Turpan; the rest of the cities were more affected by PM2.5. Potential sources included Urumqi-Changj-Shihezi, Turpan, the northern Bayingol Mongolian Autonomous Prefecture, and eastern Kazakhstan. Consequently, the emphasis on improving air quality should be on reducing local emissions, strengthening regional cooperation, and researching transboundary transport of air pollutants.

Net ecosystem productivity (NEP), which plays a key role in the carbon cycle, is an important indicator of the ecosystem\'s carbon budget. In this paper, the spatial and temporal variations of NEP over Xinjiang Autonomous Region, China from 2001 to 2020 were studied based on remote sensing and climate re-analysis data. The modified Carnegie Ames Stanford Approach (CASA) model was employed to estimate net primary productivity (NPP), and the soil heterotrophic respiration model was used to calculate soil heterotrophic respiration. Then NEP was obtained by calculating the difference between NPP and heterotrophic respiration. The annual mean NEP of the study area was high in the east and low in the west, high in the north and low in the south. The 20-year mean vegetation NEP of the study area is 128.54 gC·m-2, indicating that the study area is a carbon sink on the whole. From 2001 to 2020, the annual mean vegetation NEP ranged between 93.12 and 158.05 gC·m-2, and exhibited an increasing trend in general. 71.46% of the vegetation area showed increasing trends of NEP. NEP exhibited a positive relationship with precipitation and a negative relationship with air temperature, and the correlation with air temperature was more significant. The work reveals the spatio-temporal dynamics of NEP in Xinjiang Autonomous Region and can provide a valuable reference for assessing regional carbon sequestration capacity.

A lot of spring maize is grown in Northeast China (Liaoning, Jilin, and Heilongjiang), an area that is highly susceptible to drought. Here, remote sensing indexes from 2002 to 2020 were studied using the 8-day surface reflectance and land surface temperature of Moderate-resolution Imaging Spectroradiometer data. Spring maize distribution was extracted using a decision tree classification, and the results were compared to the known distribution based on field investigation data and published statistics. The results showed that mixed pixels of spring maize and soybeans had limited influence on the study of spatio-temporal variations of spring maize, and the error was acceptable. The overall accuracy of verifying the spring maize distribution from 2018 to 2020 was above 85%. The stable, fluctuating, and low-frequency planting areas of spring maize accounted for 11.86%, 17.41%, and 34.86% of the study area, respectively. In 2015, the government directed a reduction of the planting area of spring maize in the \"Liandaowan\" region of Northeast China. The planting area of spring maize was characterized by a continuous increase before this change (2002-2014), exhibited changes and reductions in response to the change (2015-2017), and exhibited optimization and recovery after this change (2018-2020). Compared with the fluctuating and low-frequency planting areas, moderate and severe droughts were higher in stable planting areas. From 2002 to 2020, the most severe droughts occurred in the expanded planting areas. This rapid and large-scale monitoring of spatio-temporal variations and drought of spring maize provides a foundation for improving grain yield. This method could be easily applied to the study of other regions and combined with high-resolution and hyperspectral satellite data to improve monitoring accuracy.

The adverse effects of global climate change on human production and life are becoming increasingly prominent. Responding to climate change has become a severe challenge faced by human society, and the reduction in greenhouse gas emissions has gradually become a common action by all countries. Therefore, analyzing carbon emissions through scientific methods has become an important foundation for responding to the national \"dual carbon\" strategy. This study used provincial-level carbon emission statistics, combined with nighttime light data and population data, and assigned carbon emissions to the grid scale. It also analyzed the temporal and spatial characteristics and evolution characteristics of carbon emissions in China in 2000, 2005, 2010, 2015, and 2018, as well as the correlation between carbon emissions and the economy. The results showed that:① from 2000 to 2018, the total CO2 emissions in China continued to grow, but the growth rate slowed over time. The average annual growth rate of carbon emissions dropped from 9.9% in 2000-2010 to 7.4% in 2010-2018. From the perspective of spatial distribution, carbon-free areas were mainly distributed in the northwest uninhabited area and northeast forest and mountainous areas, low-carbon emissions were mainly distributed in the vast small and medium-sized cities and towns, and high-carbon emissions were concentrated in northern, central, eastern coastal, and western provincial capitals and urban agglomerations. ② Carbon emissions had high-value or low-value agglomerations at prefecture-level cities; this agglomeration tended to stabilize as a whole and had strengthened after 2005. Low-low agglomeration areas were mainly distributed in the western contiguous areas and Hainan Island. With economic and social development, low-low agglomeration areas began to fragment and reduce in size; high-high agglomeration areas were mainly distributed in the Beijing-Tianjin-Hebei urban agglomeration, Taiyuan urban agglomeration, Yangtze River Delta urban agglomerations, and Pearl River Delta urban agglomerations, and the scale was gradually strengthened and consolidated; high-low and low-high agglomeration areas mainly appeared in neighboring cities with large differences in economic development levels. ③ Carbon emissions in most parts of China were relatively stable. The areas where carbon emissions had changed were mainly distributed in the peripheral areas of provincial capitals and key cities, and there was a circle structure with no changes in the central urban area and changes in carbon emissions in the peripheral areas. ④ The overall process of urban development in China from 2000 to 2018 followed a shift from \"low emission-low income\" to \"high emission-low income\" to \"high emission-high income\" and finally to \"low emission-high income.\" The growth rate of carbon emissions in China is slowing down. Under the background of the \"dual carbon\" strategy, different regions face different carbon emission reduction tasks and pressures due to different carbon emission situations. Therefore, the differentiated carbon emissions policy should be implemented by regions and industries.

Polycyclic aromatic hydrocarbons (PAHs) and heavy metals (HMs) are persistent environmental issues. Secondary emissions are produced as a result of climate change and human activity. To observe spatio-temporal variations of PAHs and HMs and to discuss the sources as well as the source or sink of PAHs for sediment and peat, twelve surface sediment and surface water sites were chosen along the direction of the flow to down hole in the Dajiuhu area, simultaneously, surface peat and water samples were collected in peatland. Samples were continuously taken for three years (Sep. 2018, Sep. 2019, and Sep. 2020, respectively). The results showed that PAHs and HMs are common in sediment and peat. PAHs concentration is generally higher in peat and water, while HMs concentration is relatively higher in water and relatively low in sediment and peat, and the ecological risk of sediment was low. HMs in sediment are mainly affected by rock weathering, while PAHs are mainly affected by atmospheric deposition, biomass and coal combustion and vehicle emission. HMs and PAHs can be used as an indicator of rock weathering and human activity in Dajiuhu area, respectively. A water-sediment fugacity analysis revealed that peat is a sink for PAHs, confirming that it has a high capacity for adsorbing organic contaminants, and that sediments are secondary sources of PAHs that can release them into water. Attention should be paid to the increased fugacity fraction (ff) value in peatland, indicating that peat might be converted from a sink to a source of PAHs.