Long-term Spatiotemporal Variations Research Articles

Long-term spatiotemporal variations of ammonia in the Yangtze River Delta region of China and its driving factors

This study focuses on the spatiotemporal distribution, urban-rural variations, and driving factors of ammonia Vertical Column Densities (VCDs) in China's Yangtze River Delta region (YRD) from 2008 to 2020. Utilizing data from the Infrared Atmospheric Sounding Interferometer (IASI), Generalized Additive Models (GAM), and the GEOS-Chem chemical transport model, we observed a significant increase of NH3 VCDs in the YRD between 2014 and 2020. The spatial distribution analysis revealed higher NH3 concentrations in the northern part of the YRD region, primarily due to lower precipitation, alkaline soil, and intensive agricultural activities. NH3 VCDs in the YRD region increased significantly (65.18%) from 2008 to 2020. The highest growth rate occurs in the summer, with an annual average growth rate of 7.2% during the period from 2014 to 2020. Agricultural emissions dominated NH3 VCDs during spring and summer, with high concentrations primarily located in the agricultural areas adjacent to densely populated urban zones. Regions within several large urban areas have been discovered to exhibit relatively stable variations in NH3 VCDs. The rise in NH3 VCDs within the YRD region was primarily driven by the reduction of acidic gases like SO2, as emphasized by GAM modeling and sensitivity tests using the GEOS-Chem model. The concentration changes of acidic gases contribute to over 80% of the interannual variations in NH3 VCDs. This emphasizes the crucial role of environmental policies targeting the reduction of these acidic gases. Effective emission control is urgent to mitigate environmental hazards and secondary particulate matter, especially in the northern YRD.

Long-term spatiotemporal variation in groundwater recharge in the highly irrigated semi-arid region of India: The intertwined relationship between climate variability and anthropogenic activities

Quantifying groundwater recharge in semi-arid regions is crucial for water resource management and ensuring food security. This study estimates groundwater recharge from the water table fluctuation (WTF) and tritium tracer methods and examines the impact of irrigated agriculture in Telangana, which experiences semi-arid climatic conditions. The mean recharge over the study period was 176 mm using the Water Table Fluctuation (WTF) method and 118 mm using the tritium tracer method. However, it experiences a significant decrease during drought years. Our analysis reveals that approximately 15% of the average annual precipitation (960 mm), subject to interannual variations, contributes to natural recharge. The contribution exhibits significant spatiotemporal variations influenced by both natural and anthropogenic factors. The Empirical Orthogonal Function (EOF) analyses show a significantly high correlation (0.80) between precipitation and groundwater storage. Since the hard rock crystalline aquifers comprises about 85% of the study area, the storage capacity of saprolite layers and developed fracture systems primarily limit recharge. Despite ample rainfall in two consecutive years, the recharge is significantly low in the second year. However, a drought in the previous year, followed by favorable rainfall in the current year, tends to increase recharge. Over the past three decades, the area irrigated with groundwater has increased 2.5 times, which has affected groundwater recharge rates. This study provides insights into groundwater dynamics and its association with climate variability and human activities, especially in the crystalline hard rock aquifer systems under semi-arid conditions. Understanding the interplay between hydrogeological, climate, and anthropogenic factors is crucial for implementing effective water conservation measures in water stress areas and formulating regional groundwater management policies.

Assessment of ambient particulate matter and trace gases in Istanbul: Insights from long-term and multi-monitoring stations

In this study, the long-term spatio-temporal variation, trends, and source increments of particulate matter (PM10 and PM2.5) and gaseous pollutants (SO2, NO, NO2, CO, O3) measured from 2015 to 2021 across 37 air quality monitoring stations in the European and Asian sides of Istanbul were investigated. The average PM10 concentration on the European side is higher than the Asian side. However, the average PM2.5 in both regions showed the same values. The results show that the industries near urban sites mainly affect PM10 concentrations in Istanbul. Ship emissions (from the Istanbul Strait) make the largest incremental contribution to SO2 and CO (211% and 139%, respectively) among other pollutants. PM10, PM2.5, SO2, and NO2 concentrations show significant (p < 0.01) negative temporal trends of 6.02%, 6.97%, 5.38%, and 3.28%, respectively, most notable in the winter season. However, the CO and O3 concentration trends are stable and increasing. According to the correlation coefficient (R) and the coefficient of divergence (COD) analysis, there is a heterogeneous distribution of pollutants at the intra-urban scale. PM2.5 concentrations are dominated by diverse pollution sources such as traffic, shipping and regional sources.

Spatiotemporal variation in heatwaves and elderly population exposure across China

Heatwaves have been intensified worldwide due to climate change, posing great health risks, especially to elderly populations. However, in China, limited studies have employed the heat index to decipher the spatiotemporal trends of heatwaves and their impacts on the elderly population. By comparing the three heatwave definitions, this study aimed to evaluate the long-term spatiotemporal variations in heatwaves from 1964 to 2022 across China using the Excess Heat Factor (EHF). We took advantage of high-resolution reanalysis temperature data on the Google Earth Engine (GEE) platform to efficiently calculate the heatwaves. Our results revealed that the frequency and duration of heatwaves increased significantly in approximately 77 % of China's total area, with South China experiencing the most frequent and prolonged heatwaves. Conversely, in most areas, no significant trend was discerned in the growth of the maximum and average heatwave intensities. The total number of elderly people affected by heatwaves surged from approximately 11.96 million in 2001 to over 30.31 million in 2020, with an estimated additional 1.12 million older adults exposed to heatwaves annually across the nation (R2 = 0.60, p < 0.05). The population factor exhibited largest effect on the exposure of heatwaves, followed by climate effects and combined factors, with the corresponding explanatory power about 42.84 %, 34.85 % and 22.31 %, respectively. These individuals predominantly resided in the Northeast China, Southwest China, and South China. We also found geographical variations in heatwave exposure along elevations and land use types. These insights underscore the pressing necessity for formulating strategic interventions to mitigate the health threats presented by mounting heatwave exposure, especially for susceptible groups like the elderly in China.

Long-term spatiotemporal variation in water area of the Xin'anjiang Reservoir (Lake Qiandao) from 1987 to 2022 and its relationships with water level and water storage

Long-term spatiotemporal variation in water area of the Xin'anjiang Reservoir (Lake Qiandao) from 1987 to 2022 and its relationships with water level and water storage

Extreme value analysis of precipitation and temperature over Jammu &amp; Kashmir and Ladakh in western Himalaya, India

Climate change has become a major issue for the world today. Small changes in the climate in the Himalayan region can have a significant impact on the delicate ecosystem, which is very sensitive to such changes. Recent investigations into climate change in the Western Himalayas have provided compelling evidence that these regions are especially susceptible to a wide variety of catastrophic occurrences. In the current scenario, the threat posed by climate change to human existence in Jammu and Kashmir (J&amp;K), as well as the region of Ladakh, has grown more tangible and evident. Temperature and precipitation statistics could be used to observe this regional climatic shift. This study analyses and forecasts long-term spatio-temporal variations in precipitation and temperature using a century-long dataset from 1901 to 2002 over 14 districts of Jammu and Kashmir and Ladakh. The Augmented Dickey-Fuller (ADF) test and the Kwiatkowski-Phillips-Schmidt-Shin (KPSS) test of stationarity on the data show that the time series is stationary. Extreme Value Theory (EVT), which is an outstanding statistical method to interpret the records for the estimation of the future probability of the occurrence of extremes, is utilised in this study. Further, precipitation and temperature extremes are forecasted for 50, 80, 100, 120, 200, 250, 300, and 500 year return periods respectively and results reveal that the districts- Jammu, Rajouri, Leh, Srinagar, Baramulla and Poonch will be more prone to extreme weather events phenomenon.

Patterns and predictors of racial/ethnic disparities in HIV care continuum in the Southern USA: protocol for a population-based cohort study

IntroductionHealth disparities exist at every step of the HIV care continuum (HCC) among racial/ethnic minority population. Such racial/ethnic disparities may have significantly delayed the progress in HCC in the Southern...

Estimating morning and evening commute period O3 concentration in Taiwan using a fine spatial-temporal resolution ensemble mixed spatial model with Geo-AI technology

Elevated levels of ground-level ozone (O3) can have harmful effects on health. While previous studies have focused mainly on daily averages and daytime patterns, it's crucial to consider the effects of air pollution during daily commutes, as this can significantly contribute to overall exposure. This study is also the first to employ an ensemble mixed spatial model (EMSM) that integrates multiple machine learning algorithms and predictor variables selected using Shapley Additive exExplanations (SHAP) values to predict spatial-temporal fluctuations in O3 concentrations across the entire island of Taiwan. We utilized geospatial-artificial intelligence (Geo-AI), incorporating kriging, land use regression (LUR), machine learning (random forest (RF), categorical boosting (CatBoost), gradient boosting (GBM), extreme gradient boosting (XGBoost), and light gradient boosting (LightGBM)), and ensemble learning techniques to develop ensemble mixed spatial models (EMSMs) for morning and evening commute periods. The EMSMs were used to estimate long-term spatiotemporal variations of O3 levels, accounting for in-situ measurements, meteorological factors, geospatial predictors, and social and seasonal influences over a 26-year period. Compared to conventional LUR-based approaches, the EMSMs improved performance by 58% for both commute periods, with high explanatory power and an adjusted R2 of 0.91. Internal and external validation procedures and verification of O3 concentrations at the upper percentile ranges (in 1%, 5%, 10%, 15%, 20%, and 25%) and other conditions (including rain, no rain, weekday, weekend, festival, and no festival) have demonstrated that the models are stable and free from overfitting issues. Estimation maps were generated to examine changes in O3 levels before and during the implementation of COVID-19 restrictions. These findings provide accurate variations of O3 levels in commute period with high spatiotemporal resolution of daily and 50m * 50m grid, which can support control pollution efforts and aid in epidemiological studies.

Long-term spatiotemporal variations in surface NO2 for Beijing reconstructed from surface data and satellite retrievals

Remote sensing data from the Ozone Monitoring Instrument (OMI) and the TROPOspheric Monitoring Instrument (TROPOMI) play important roles in estimating surface nitrogen dioxide (NO2), but few studies have compared their differences for application in surface NO2 reconstruction. This study aims to explore the effectiveness of incorporating the tropospheric NO2 vertical column density (VCD) from OMI and TROPOMI (hereafter referred to as OMI and TROPOMI, respectively, for conciseness) for deriving surface NO2 and to apply the resulting data to revisit the spatiotemporal variations in surface NO2 for Beijing over the 2005–2020 period during which there were significant reductions in nitrogen oxide emissions. In the OMI versus TROPOMI performance comparison, the cross-validation R2 values were 0.73 and 0.72, respectively, at 1 km resolution and 0.69 for both at 100 m resolution. The comparisons between satellite data sources indicate that even though TROPOMI has a finer resolution it does not improve upon OMI for deriving surface NO2 at 1 km resolution, especially for analyzing long-term trends. In light of the comparison results, we used a hybrid approach based on machine learning to derive the spatiotemporal distribution of surface NO2 during 2005–2020 based on OMI. We had novel, independent passive sampling data collected weekly from July to September of 2008 for hindcasting validation and found a spatiotemporal R2 of 0.46 (RMSE = 7.0 ppb). Regarding the long-term trend of surface NO2, the level in 2008 was obviously lower than that in 2007 and 2009, as expected, which was attributed to pollution restrictions during the Olympic Games. The NO2 level started to steadily decline from 2015 and fell below 2008's level after 2017. Based on OMI, a long-term and fine-resolution surface NO2 dataset was developed for Beijing to support future environmental management questions and epidemiological research.

Projecting future aboveground carbon sequestration rate of alpine forest on the eastern Tibetan Plateau in response to climate change.

The aboveground carbon sequestration rate (ACSR) of forests serves as an indicator of their carbon sequestration capacity over time, providing insights into the potential carbon sequestration capacity of forest ecosystems. To explore the long-term Spatiotemporal variation of ACSR in the transitional ecotone of theeastern Tibetan Plateau under climate change scenarios, we utilized a forest landscape model that was parameterized with forest inventory data from the eastern Tibetan Plateau to simulate this ecological function changes. The study found that climate warming had significant effect on forests ACSR in different types of forests. ACSR was significantly reduced (p<0.05) in cold temperate coniferous and temperate coniferous forests, whereas it was significantly increased in deciduous broad-leaved forests. However, the impact of climate warming on evergreen broad-leaved forests was found to be negligible. At the species level, climate warming has mostly suppressed the ACSR of coniferous trees, except for Chinese hemlock. The main dominant species, spruce and fir, have been particularly affected. Conversely, the ACSR of most broad-leaved trees has increased due to climate warming. In addition, at the landscape scale, the ACSR within this region is expected to experience a steady decline after 2031s-2036s. Despite the effects of climate warming, this trend is projected to persist. In conclusion, the forests ACSR in this region will be significantly affected by future climate warming. Our research indicates that climate warming will have a noticeable suppressive effect on conifers. It is imperative that this factor be taken into account when devising forest management plans for the future in this region.

Regional variation in climate change alters the range-wide distribution of colour polymorphism in a wild bird.

According to Gloger's rule, animal colouration is expected to be darker in wetter and warmer climates. Such environmental clines are predicted to occur in colour polymorphic species and to be shaped by selection if colour morphs represent adaptations to different environments. We studied if the distribution of the colour polymorphic tawny owl (Strix aluco) morphs (a pheomelanic brown and a pale grey) across Europe follow the predictions of Gloger's rule and if there is a temporal change in the geographical patterns corresponding to regional variations in climate change. We used data on tawny owl museum skin specimen collections. First, we investigated long-term spatiotemporal variation in the probability of observing the colour morphs in different climate zones. Second, we studied if the probability of observing the colour morphs was associated with general climatic conditions. Third, we studied if weather fluctuations prior to the finding year of an owl explain colour morph in each climate zone. The brown tawny owl morph was historically more common than the grey morph in every studied climate zone. Over time, the brown morph has become rarer in the temperate and Mediterranean zone, whereas it has first become rarer but then again more common in the boreal zone. Based on general climatic conditions, winter and summer temperatures were positively and negatively associated with the proportion of brown morph, respectively. Winter precipitation was negatively associated with the proportion of brown morph. The effects of 5-year means of weather on the probability to observe a brown morph differed between climate zones, indicating region-dependent effect of climate change and weather on tawny owl colouration. To conclude, tawny owl colouration does not explicitly follow Gloger's rule, implying a time and space-dependent complex system shaped by many factors. We provide novel insights into how the geographic distribution of pheomelanin-based colour polymorphism is changing.

Estimating the daily average concentration variations of PCDD/Fs in Taiwan using a novel Geo-AI based ensemble mixed spatial model

It is generally established that PCDD/Fs is harmful to human health and therefore extensive field research is necessary. This study is the first to use a novel geospatial-artificial intelligence (Geo-AI) based ensemble mixed spatial model (EMSM) that integrates multiple machine learning algorithms and geographic predictor variables selected using SHapley Additive exPlanations (SHAP) values to predict spatial-temporal fluctuations in PCDD/Fs concentrations across the entire island of Taiwan. Daily PCDD/F I-TEQ levels from 2006 to 2016 were used for model construction, while external data was used for validating model dependability. We utilized Geo-AI, incorporating kriging, five machine learning, and ensemble methods (combinations of the aforementioned five models) to develop EMSMs. The EMSMs were used to estimate long-term spatiotemporal variations in PCDD/F I-TEQ levels, considering in-situ measurements, meteorological factors, geospatial predictors, social and seasonal influences over a 10-year period. The findings demonstrated that the EMSM was superior to all other models, with an increase in explanatory power reaching 87 %. The results of spatial-temporal resolution show that the temporal fluctuation of PCDD/F concentrations can be a result of weather circumstances, while geographical variance can be the result of urbanization and industrialization. These results provide accurate estimates that support pollution control measures and epidemiological studies.

Long-term spatiotemporal variation in atmospheric aerosol properties over Türkiye based on MERRA-2 reanalysis data: aerosol classification based on city type.

Due to their complex aerosol characteristics, it is crucial to analyze the trends and properties of atmospheric aerosols over the eastern Mediterranean countries. This study comprehensively evaluates Aerosol Optical Depth (AOD) and Angström Exponent (AE) trends and aerosol classification over Türkiye, using the MERRA-2 reanalysis data from 1980 to 2019. The spatial distributions of AOD and AE were determined across various temporal scales, including multiannual, 5-year intervals, seasonal, and monthly periods. The analysis of the spatial distribution of AOD values revealed that the mean values in the northwestern areas, ranging from 0.20 to 0.25, were comparatively higher than those observed in the eastern regions, which ranged from 0.10 to 0.15. Between 1980 and 1994, the AOD values gradually increased, followed by a subsequent decline from 1995 to 2019. Based on 5-year intervals between 1980 and 2019, the coastal regions exhibited higher AOD values than the inland areas. Specifically, higher AOD values were noted between May and August, whereas lower values were observed during autumn and winter. Additionally, higher AE values were detected over the northwestern region, while the southeastern region had the lowest AE values, particularly during spring, attributed to the frequent occurrence of dust transport events in this area. The AOD and AE values were also examined in different city types, using the population thresholds of the European Commission. The global city category consisting only of Istanbul showed the highest AOD values across all seasons, while the category of very small cities, which includes 12 cities, had the lowest AOD values. Furthermore, this study investigated the contributions of dominant aerosol categories across various city types based on multiannual and seasonal variations of AOD and AE. The results showed that mixed and continental aerosols had higher portions across all city types. However, biomass burning/industrial and mixed aerosol categories were more prominent in global and large cities. Overall, this study provides a comprehensive overview of the atmospheric aerosol properties in Türkiye and can serve as a useful guide for researchers intending to conduct future studies utilizing AOD and AE data obtained through MERRA-2 aerosol diagnosis.

Long-term remote observations of particulate organic phosphorus concentration in eutrophic Lake Taihu based on a novel algorithm

Monitoring the long-term spatiotemporal variations in particulate organic phosphorus concentration (CPOP) is imperative for clarifying the phosphorus cycle and its biogeochemical behavior in waters. However, little attention has been devoted to this owing to a lack of suitable bio-optical algorithms that allow the application of remote sensing data. In this study, based on Moderate Resolution Imaging Spectroradiometer (MODIS) data, a novel absorption-based algorithm of CPOP was developed for eutrophic Lake Taihu, China. The algorithm yielded a promising performance with a mean absolute percentage error of 27.75% and root mean square error of 21.09 μg/L. The long-term MODIS-derived CPOP demonstrated an overall increasing pattern over the past 19 years (2003–2021) and a significant temporal heterogeneity in Lake Taihu, with higher value in summer (81.97 ± 3.81 μg/L) and autumn (82.07 ± 3.8 μg/L), and lower CPOP in spring (79.52 ± 3.81 μg/L) and winter (78.74 ± 3.8 μg/L). Spatially, relatively higher CPOP was observed in the Zhushan Bay (85.87 ± 7.5 μg/L), whereas the lower value was observed in the Xukou Bay (78.95 ± 3.48 μg/L). In addition, significant correlations (r > 0.6, P < 0.05) were observed between CPOP and air temperature, chlorophyll-a concentration and cyanobacterial blooms areas, demonstrating that CPOP was greatly influenced by air temperature and algal metabolism. This study provides the first record of the spatial-temporal characteristics of CPOP in Lake Taihu over the past 19 years, and the CPOP results and regulatory factors analyses could provide valuable insights for aquatic ecosystem conservation.

Long-term variations in nutritional condition of Panulirus argus (Decapoda: Palinuridae) in Cuba: analytical and morphological approaches

Nutritional condition indices have been frequently used as morphophysiological indicators to study variations on lobster's physiology and energy reserves. This study was aimed to determine long-term spatio-temporal variations in nutritional conditions of Panulirus argus (hereinafter – lobster), considering two indices, namely the analytic Blood Refractive Index (BRI) and the morphometric Kcl (total weight / carapace length relationship). Information from eleven sites in the Caibarién region, sampled in 2010–2015, and twelve sites in the Batabanó region, sampled in 2011–2017, were named as the Current Period. Morphometric data for Kcl estimation, including 1987–1988 in the Caibarién region, and 1981–1993 in the Batabanó region, were grouped as the Past Period. Temporary variations were determined comparing Past and Current periods at each region. Spatial variations were determined by comparing the data between the two regions. For analysed lobsters in the Current period, we determined BRI = 14.3, Kcl = 7.0 in the Caibarién region, and BRI = 15.2. Kcl = 7.0 in the Batabanó region. For the Past period, the Kcl value was 5.8 and 6.3 in the Caibarién and Batabanó region, respectively. Both Kcl and BRI indices were higher for male individuals. This could be associated with the reproductive process, and intrinsic morphometric differences between sexes could influence though. Morphometric parameters were higher for the Current Period. Spatial variations of morphometric parameters were significantly different for the Past Period, which were attributed to different environmental conditions in each region. The BRI index was higher in the Batabanó region, which was possibly associated with the better status of the benthic communities and the better water quality. Present results contribute to understanding lobster's nutritional condition in natural habitats, mainly in the Caibarién region, where few studies have been carried out to date.

Mapping contiguous XCO2 by machine learning and analyzing the spatio-temporal variation in China from 2003 to 2019

As China is the world's largest CO2 emitter, it is important to understand the spatio-temporal variation of atmospheric CO2 to reduce carbon emissions. Satellite remote sensing for carbon monitoring has been widely used and studied because of its long-term and large-scale characteristics. However, the satellite data results are very sparse with significant gaps due to narrow swath and other factors on CO2 retrieval. The simple interpolation methods ignore the influential factors of CO2 and loss the spatial resolution, which leads to the inability to quantify the spatio-temporal variation well. This study developed a machine learning method that considers carbon emissions, vegetation, and meteorology. Using the column-averaged dry-air mole fraction of CO2 (XCO2) data of SCIAMACHY, GOSAT, and OCO-2, we derived monthly-scale contiguous XCO2 data across China from 2003 to 2019 with 0.25° resolution. The results showed a good agreement with the satellite measurements, with the bias and standard deviation of 0.11 and 1.38 ppmv for the validation dataset, respectively. Moreover, the results were consistent with the model simulation and in-situ sites, indicating the ability to reflect long-term spatio-temporal variation with a finer texture. We analyzed the spatial distribution, seasonal variation, and long-term trends of XCO2 in China, revealing that the machine learning method has comparable performance to model simulations. The results showed that XCO2 is dominated by anthropogenic emissions spatially and has a clear seasonal cycle, with a larger amplitude the further north. The long-term trend shows the XCO2 increased by an average rate of 2.17 ppmv per year from 2003 to 2019 in China, which is consistent with the global. The method and data can further study the carbon cycle and climate change.

Long-term spatiotemporal variation in density of a tropical folivore: responses to a complex disturbance regime.

The Anthropocene is a time of unprecedented and accelerating rates of environmental change that includes press (e.g., climate change) and pulse disturbances (e.g., cyclonic storms, land use change) that interact to affect spatiotemporal dynamics in the density, distribution, and biodiversity of organisms. We leverage three decades of spatially explicit data on the density of a tropical folivore (Lamponius portoricensis [Insecta, Phasmida]) in a hurricane-mediated ecosystem (montane rainforest of Puerto Rico), along with associated environmental attributes, to disentangle the effects of interacting disturbances at multiple spatial scales. Spatiotemporal variation in density at a small spatial scale is affected by disturbance-related characteristics (hurricane severity, time after most recent major hurricane, ambient temperature, and understory temperature), legacies of previous land use, and understory habitat structure. Nonetheless, only a small proportion of spatiotemporal variation in density was related to those characteristics. In contrast, the majority of interannual variation in mean density at a larger scale was related to disturbance characteristics and understory habitat structure. These factors combine to affect a weak and declining trend in the density of L. portoricensis over time. The low resistance of L. portoricensis to Hurricane Hugo, as compared to Hurricanes Georges and Maria, likely arose because a drought followed Hurricane Hugo. The disturbance regime of the region is predicted to include increases in ambient temperatures, frequency of high-intensity storms, and frequency of droughts. Such trends may combine to threaten the conservation status of L. portoricensis, and other species with which it shares similar life history characteristics.

Variation of satellite-derived total suspended matter in large lakes with four types of water storage across the Tibetan Plateau, China

Total suspended matter (TSM), as an indicator of the concentration of fine materials in the water column including particulate nutrients, pollutants, and heavy metals, is widely used to monitor aquatic ecosystems. However, the long-term spatiotemporal variations of TSM in lakes across the Tibetan Plateau (TP) and their response to environmental factors are rarely explored. Accordingly, taking advantage of the Landsat top-of-atmosphere reflectance and in-situ data, an empirical model (R2 = 0.83, RMSE = 1.08 mg/L, and MAPE = 19.49 %) was developed to estimate the average autumnal TSM in large TP lakes (≥50 km2) during the 1990–2020 period. For analyzing the spatiotemporal variability in TP lakes TSM, the examined lakes were classified into four types (Type A-D) based on their water storage changing in different periods. The results showed that the lakes in the southern and some northeastern parts of the TP exhibited lower TSM values than those situated in other regions. The assessment of TSM in each of these four lake types showed that more than half of them had a TSM value of <20 mg/L. Apart from Type D, the lakes with the TSM showing significantly decreasing trends were dominantly Types A-C. A relative contribution analysis involving five driving factors indicated that they contributed by >50 % to lake TSM interannual variation in 73 out of 114 watersheds, and the lakes area change demonstrated the greatest contribution (82.2 %), followed by wind speed (11.0 %). Further comparison between the entire lake and the non-expansive regions suggested that the expansive region played an indispensable role in determining the TSM value of the whole lake. This study can help to better understand the water quality condition and provide valuable information for policy-makers to maintain sustainable development in the TP region.

Long-term water clarity patterns of lakes across China using Landsat series imagery from 1985 to 2020

Abstract. Monitoring the water clarity of lakes is essential for the sustainable development of human society. However, existing water clarity assessments in China have mostly focused on lakes with areas &gt; 1 km2, and the monitoring periods were mainly in the 21st century. In order to improve the understanding of spatiotemporal variations in lake clarity across China, based on the Google Earth Engine cloud platform, a 30 m long-term LAke Water Secchi depth (SD) dataset (LAWSD30) of China (1985–2020) was first developed using Landsat series imagery and a robust water-color parameter-based SD model. The LAWSD30 dataset exhibited a good performance compared to concurrent in situ SD datasets, with an R2 of 0.86 and a root mean square error of 0.225 m. Then, based on our LAWSD30 dataset, long-term spatiotemporal variations in SD for lakes &gt; 0.01 km2 (N = 40 973) across China were evaluated. The results show that the SD of lakes with areas ≤ 1 km2 exhibited a significant downward trend in the period of 1985–2020, but the decline rate began to slow down and stabilized after 2001. In addition, the SD of lakes with an area &gt; 1 km2 showed a significant downward trend before 2001, and began to increase significantly afterwards. Moreover, in terms of the spatial patterns, the proportion of small lakes (area ≤ 1 km2) showing a decreasing SD trend was the largest in the Mongolian–Xinjiang Plateau Region (MXR) (about 30.0 %), and the smallest in the Eastern Plain Region (EPR) (2.6 %). In contrast, for lakes &gt; 1 km2, this proportion was the highest in MXR (about 23.0 %), and the lowest in the Northeast Mountain Plain Region (NER) (16.1 %). The LAWSD30 dataset and the spatiotemporal patterns of lake water clarity in our research can provide effective guidance for the protection and management of lake environment in China.

Analysis of the Characteristics of Ozone Pollution in the North China Plain from 2016 to 2020

As a major gaseous pollutant, ozone (O3) adversely affects human health and ecosystems. In recent years, ozone pollution in China has gradually become a prominent issue, especially in the North China Plain (NCP). To study the long-term spatio-temporal variation patterns of O3 in the NCP, this study selected 230 monitoring stations in the NCP from 2016 to 2020 as research objects, used the Kriging interpolation method and global Moran’s index to discuss the spatial-temporal distribution of O3, combining meteorological and social statistical data to analyze the causes underlying regional differences. The temporal analysis demonstrated that the O3-8h average concentrations increased annually from 2016 to 2018 and decreased from 2019 to 2020. The O3 concentrations were higher in spring and summer (117.89–154.20 μg/m3) and lower in autumn and winter (53.81–92.95 μg/m3). The spatial analysis revealed that O3 concentrations were low in the north and south of the NCP but high in the central area. The spatial distribution of O3 exhibited considerable cross-seasonal variability. Both meteorological conditions of high temperature and low pressure increased O3 concentrations. The spatial distribution of O3 varied depending on the period. However, the central and western regions of the Shandong Province were constantly characterized by high O3 concentrations. This pattern has been likely formed by heavy industry in the Shandong Province, as large-scale industrial production and frequent traffic flows produce a large amount of precursors, thereby exacerbating regional O3 pollution. These characteristics were attributed to emission reduction policies, meteorological conditions, the emission intensity of anthropogenic sources, and regional transport in the NCP. Overall, for cities with heavy industrial facilities in the central NCP, a timely adjustment of the energy and industrial structure, effectively controlling the emission of precursors, promoting new clean energy, and strengthening regional joint prevention and control are effective ways to alleviate O3 pollution.