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.

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.

Role and environmental regulation of iron-driven anaerobic methane oxidation in riverine sediment.

Iron is an abundant element in the environment and acts as a thermodynamically favorable electron acceptor driving the anaerobic oxidation of methane (AOM). Presently, the role and environmental regulation of iron-driven AOM in rivers, an important source of methane emission, are nearly unknown. Here, we provided direct evidence for iron-driven AOM activity in sediment of a mountainous river (Wuxijiang River, China) through 13C-labeled isotopic experiment. The potential rate of iron-driven AOM ranged between 0.40 and 1.84 nmol 13CO2 g (sediment) d-1, which contributed 36% on average to total AOM activity when combined the potential nitrate- and nitrite-driven AOM rates measured previously. There were significant variations in iron-driven AOM rates among different reaches (upper, middle, and lower) and between seasons (summer and winter). Sediment temperature, pH, and nitrate content were closely associated with the dynamic of AOM activity. Our results indicate that iron-driven AOM has great potential for reducing methane emissions from riverine ecosystems, and suggest the necessity of taking both spatial and temporal scales into account to evaluate the quantitative role of this AOM process.

Metabolic processes drive spatio-temporal variations of carbon sink/source in a karst river.

Riverine carbon dioxide (CO2) exchange is a crucial component of the global carbon cycle. However, the changes in the CO2 sink/source in karst rivers caused by differences in lithological features and climate, hindered the resolution of the spatio-temporal heterogeneity of global inland water carbon emissions. Here, we use hydrochemical data and CO2 gas isotopic data to reveal the spatio-temporal variations of CO2 sink/source in karst rivers and their controlling mechanisms. Fifty-two monitoring transects were set up in the subtropical Lijiang River in southwest China in June and December 2019. Our results indicated that the CO2 flux across the water-air interface (FCO2) in the Lijiang River basin ranged from -43.77 to 519.67 mmol/(m2·d). In June, the Lijiang River acted as an atmospheric carbon source due to higher water temperatures (Twater). However, driven by hydrodynamic conditions and the metabolism of aquatic photosynthesis, the river shifts from being an atmospheric carbon source in June to an atmospheric carbon sink in December. The stable isotopes of CO2 (δ13C-CO2) show significant differences in the spatio-temporal variations of CO2 sink/source. In December, the transects of the Lijiang River basin with a negative CO2 flux are significantly negatively correlated with dissolved oxygen (DO) and chlorophyll-a (Chl-a) concentration (p < 0.05). This confirms that the enhancement of aquatic photosynthesis efficiency increased water DO concentrations, which resulted in the positive movement of water δ13C-CO2 and a decrease in the partial pressure of CO2 (pCO2) and FCO2. Comparative analysis with global river FCO2 indicates that under the combined driving forces of metabolic processes of aquatic photosynthetic organisms and hydrodynamic conditions, rivers tend to act more frequently as CO2 sinks, particularly in subtropical and temperate rivers. In conclusion, this study represents a new example focusing on CO2 dynamics to address the spatio-temporal heterogeneity of carbon emissions in inland waters on a global scale.

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.

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.

Microbiota of the Digestive Glands and Extrapallial Fluids of Clams Evolve Differently Over Time Depending on the Intertidal Position.

The Manila clam (Ruditapes philippinarum) is the second most exploited bivalve in the world but remains threatened by diseases and global changes. Their associated microbiota play a key role in their fitness and acclimation capacities. This study aimed at better understanding the behavior of clam digestive glands and extrapallial fluids microbiota at small, but contrasting spatial and temporal scales. Results showed that environmental variations impacted clam microbiota differently according to the considered tissue. Each clam tissue presented its own microbiota and showed different dynamics according to the intertidal position and sampling period. Extrapallial fluids microbiota was modified more rapidly than digestive glands microbiota, for clams placed on the upper and lower intertidal position, respectively. Clam tissues could be considered as different microhabitats for bacteria as they presented different responses to small-scale temporal and spatial variabilities in natural conditions. These differences underlined a more stringent environmental filter capacity of the digestive glands.

Risk Control Values and Remediation Goals for Benzo[a]pyrene in Contaminated Sites: Sectoral Characteristics, Temporal Trends, and Empirical Implications.

Benzo[a]pyrene (BaP) is a highly carcinogenic pollutant of global concern. There is a need for a comprehensive assessment of regulation decisions for BaP-contaminated site management. Herein, we present a quantitative evaluation of remediation decisions from 206 contaminated sites throughout China between 2011 and 2021 using the cumulative distribution function (CDF) and related statistical methodologies. Generally, remediation decisions seek to establish remediation goals (RGs) based on the risk control values (RCVs). Cumulative frequency distributions, followed non-normal S-curve, emerged multiple nonrandom clusters. These clusters are consistent with regulatory guidance values (RGVs), of national and local soil levels in China. Additionally, priority interventions for contaminated sites were determined by prioritizing RCVs and identifying differences across industrial sectors. Notably, we found that RCVs and RGs became more relaxed over time, effectively reducing conservation and unsustainable social and economic impacts. The joint probability curve was applied to model decision values, which afforded a generic empirically important RG of 0.57 mg/kg. Overall, these findings will help decision-makers and governments develop appropriate remediation strategies for BaP as a ubiquitous priority pollutant.

Estimating the Mass of Pharmaceuticals Harbored in Municipal Solid Waste Landfills by Analyzing Refuse Samples at Various Ages and Depths.

Pharmaceuticals have been detected at high concentrations in municipal solid waste (MSW) landfill leachates, which are recognized as an underestimated source of pharmaceutical residues in the environment. However, limited efforts have been made to characterize pharmaceuticals in MSW landfill refuse, which is also of significant concern given the potential long-term environmental impact. Herein, we excavated landfill refuse from six cells with landfill ages of 7-27 years in the largest MSW landfill in Shanghai (in each cell, landfill refuse was collected from different depths of 2-8 m) and analyzed samples for the presence of 55 pharmaceuticals, including antibiotics and non-antibiotics. The results reveal the presence of 42 pharmaceuticals in landfill refuse, with median concentrations ranging from 0.30 to 116 µg/kg. Antibiotic and non-antibiotic pharmaceuticals exhibited diverse concentration trends with age, related to changes in policy intervention and consumption over time. Different concentration variations of individual pharmaceuticals were observed in refuse samples excavated at different depths and positively correlated to their sorption ability. The mass of pharmaceuticals in the investigated landfill was estimated from the obtained concentrations to be 80-220 tons with 95% probability, based on Monte Carlo analysis. To the best of our knowledge, this study provides the first estimate of pharmaceutical mass in an MSW landfill. The results will be helpful for understanding the potential long-term environmental impact of pharmaceuticals in landfills.

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 Variations Rather than Long-Term Warming Control Extracellular Enzyme Activities and Microbial Community Structures in the High Arctic Soil.

In Arctic soils, warming accelerates decomposition of organic matter and increases emission of greenhouse gases (GHGs), contributing to a positive feedback to climate change. Although microorganisms play a key role in the processes between decomposition of organic matter and GHGs emission, the effects of warming on temporal responses of microbial activity are still elusive. In this study, treatments of warming and precipitation were conducted from 2012 to 2018 in Cambridge Bay, Canada. Soils of organic and mineral layers were collected monthly from June to September in 2018 and analyzed for extracellular enzyme activities and bacterial community structures. The activity of hydrolases was the highest in June and decreased thereafter over summer in both organic and mineral layers. Bacterial community structures changed gradually over summer, and the responses were distinct depending on soil layers and environmental factors; water content and soil temperature affected the shift of bacterial community structures in both layers, whereas bacterial abundance, dissolved organic carbon, and inorganic nitrogen did so in the organic layer only. The activity of hydrolases and bacterial community structures did not differ significantly among treatments but among months. Our results demonstrate that temporal variations may control extracellular enzyme activities and microbial community structure rather than the small effect of warming over a long period in high Arctic soil. Although the effects of the treatments on microbial activity were minor, our study provides insight that microbial activity may increase due to an increase in carbon availability, if the growing season is prolonged in the Arctic.

Spatial patterns and temporal variations of pollutants at 56 air quality monitoring stations in the state of São Paulo, Brazil.

This study applied two data mining tasks: clustering and association rules to a dataset of pollutants in the state of São Paulo. The clustering task was applied to temporal patterns and geospatial distributions of pollutants, and the association rules were used to identify prevailing meteorological conditions when there were high concentrations of pollutants from 2017 to 2019. The results indicated good adequacy of the cluster, indicating different pollution levels per group, with a silhouette coefficient from 0.26 to 0.72. In the spatial evaluation, the groups severely polluted were located in the metropolitan region, on the coast and, some inland cities, by industrial, vehicular, burning, agriculture, and other emissions. The cluster identified a strong presence of O3 and PM2.5 in 65% and 72% of the monitored stations in several areas of the state. As for the distance between the sources of pollution, the groups of PM10 and NO2 were geographically distant, while PM2.5, CO, SO2, and O3 were closer, suggesting a spatial relationship of exposure. Seasonality was similar between groups, with significantly higher concentrations in winter, except for O3, for which higher concentrations occurred in summer. Meteorological conditions contributed to critical episodes of pollution (support and confidence greater than 80%), with low temperature and humidity, low rainfall, and milder wind associated with increased pollutants. In conclusion, investigating spatial representativeness allows revealing spatial and temporal patterns of pollutants and unfavorable meteorological conditions to diffusion. Thus, ideal and effective measures can be taken to avoid critical periods of exposure based on the behavior of pollutants in different regions and related climate changes.

Study of Spatial and Temporal Variations of Ionospheric Total Electron Content in Japan, during 2014-2019 and the 2016 Kumamoto Earthquake.

There are a large number of excellent research cases in Global Navigation Satellite System (GNSS) positioning and disaster prediction in Japan region, where the simulation and prediction of total electron content (TEC) is a powerful research method. In this study, we used the data of the GNSS Earth Observation Network (GEONET) established by the Geographical Survey Institute of Japan (GSI) to compare the performance of two regional ionospheric models in Japan, in which the spherical cap harmonic (SCH) model has the best performance. In this paper, we investigated the spatial and temporal variations of ionospheric TEC in Japan and their relationship with latitude, longitude, seasons, and solar activity. The results show that the TEC in Japan increases as the latitude decreases, with the highest average TEC in spring and summer and the lowest in winter, and has a strong correlation with solar activity. In addition, the observation and analysis of ionospheric disturbances over Japan before the 2016 Kumamoto earthquake and geomagnetic storms showed that GNSS observing of ionospheric TEC seems to be very effective in forecasting natural disasters and monitoring space weather.

Spatial and temporal analysis of water quality from 1991 to 2011 in Miyun Reservoir.

Cluster analysis, discriminant analysis-DA, and principal component analysis/factor analysis were used to analyze temporal-spatial variations and sources of water quality from 1991 to 2011 in the Miyun Reservoir. Water quality analysis was conducted in three interannual (IA) groups: IA I (1991-1993, 1995), IA II (1994, 1996-2000, 2002-2006), and IA III (2001, 2007-2011); two seasonal clusters: non-flood season (NF, November-December and January-April) and flood season (FL, May-October); and two spatial clusters (MP-main part of Kuxi and Kudong and NH-Neihu), based on spatial I (I-Kudong, I-Kuxi, and I-Neihu), spatial II (II-Kudong, II-Kuxi, and II-Neihu), spatial III (III-Kudong, III-Kuxi, and III-Neihu), spatial NF (NF-Kudong, NF-Kuxi, and NF-Neihu), and spatial FL (FL-Kudong, FL-Kuxi, and FL-Neihu). Spatial variations between MP and NH were lower than those between seasonal and IA variations. IA DA showed that electrical conductivity (EC), Ca2+, Mg2+, T-Hard, and T-Alk were due to carbonate dissolution accelerated by SO42- both from fertilizer use and industrial activities. Effective control measures decreased BOD5 and NO3--N. Spatial variations at spatial IA and seasonal scales showed that high levels of significant parameters in MP were mostly attributed to non-point pollution from watershed, whereas cage culture and sediment release in NH. The main pollution was comprised of nitrogen, phosphorus, organic, and other ion pollutants (Ca2+, SO42-, Mg2+, T-Alk, EC, and T-Hard). Future studies must focus on water circulation enhancement, timely sediment dredging, and decreasing non-point pollution in FL (water and soil loss, fertilizer use, and cage culture) and anthropogenic discharge in NF.

Analyzing WSTP trend: a new method for global warming assessment.

This paper tries to introduce a time-series of temperature parameters as a potential method for studying the global warming. So, we investigated the spatial-temporal variations of warm-season temperature parameters (WSTP), including start time, end time, length of season, base value, peak time, peak value, amplitude, large integrated value, right drive, and left drive, using a database of 30 years' period in different climates of Iran. We used daily temperature data from 1989 to 2018 over Iran to extract the parameters by TIMESAT software. We studied the trend analysis of WSTP through the Mann-Kendall method. Then, we considered the Pearson correlation coefficient to calculate the correlation between WSTP and time. We assessed the trends of the slope using a simple linear regression method. Then, we compared the results of the WSTP trend analysis in climatic zones. Our results accused the hyper-arid climatic zone has the longest warm season (194.89 days a year). The warm season in this region starts earlier than other regions and increases with moderate speed (left drive, 0.19 °C day-1). Then, it reaches a peak value (31.3 °C) earlier than the different climatic zones. On the other hand, the humid regions' warm season starts with the shortest length and ends later than the other climatic zones (112.1 and 297.5 days a year for start and end times, respectively). We detected that the trend of the start time parameter has decreased by 98.02% of the study area during the last 30 years. The base value, length, and large integrated value parameters have an increasing trend of 66.47%, 80.11%, and 92.95% in Iran. The highest correlation coefficient with time was for start time and large integrated value parameters. Hence, the start time and large integrated value parameters have almost the most negative (< - 0.5) and positive (> 5) trend slope, among other parameters, respectively. In general, these results demonstrate that the studied region has faced global warming impacts over time by increasing the warm season and thermal energy, especially in arid and hyper-arid. We highlight the necessity of planning the land use under the high natural vulnerability of the studied local, especially in this new age of global warming.

Occurrence, Profiles and Ecological Risk of Bisphenol Analogues in a Municipal Sewage Treatment Plant.

Due to the strict control on bisphenol A (BPA) in many countries, bisphenol analogues (BPs) are being widely used as alternative materials to manufacture epoxy resins and polycarbonate plastics, resulting in their occurrence in sewage treatment plants (STPs). In this study, the occurrence and distribution of 7 BPs in a large-scale STP in Beijing China was investigated. Wastewater samples were collected from the influents and effluents of each processing unit, and extracted by solid-phase extraction. Target compounds were quantified by ultra-performance liquid chromatography tandem mass spectrometry (HPLC-MS/MS). The total concentrations of seven BPs (ΣBPs) were 400.42 ± 48.12 ng/L in the raw sewage, 438.60 ± 46.50 ng/L in the primary effluent, 17.21 ± 13.12 ng/L in the secondary effluent, and 11.33 ± 4.84 ng/L in the tertiary effluent, respectively. Bisphenol S (BPS) and BPA were the predominant congener in raw sewage with an overall contribution of 29.32% and 70.22% to the ΣBPs, indicating that there was a large amount of BPS and BPA consumption in the study area. During a one-week sampling period, ΣBPs changed slightly at the same sampling site. It was found that high removal efficiencies were achieved for BPs in anoxic and oxic secondary clarifier treatment units, suggesting that biodegradation and sorption played major roles in BPs elimination in the STP. After tertiary treatment, all BPs except BPA were completely removed, suggesting the necessity to investigate the fate and toxicity of BPA in the aquatic environment.

Spatiotemporal variations of ambient air pollutants and meteorological influences over typical urban agglomerations in China during the COVID-19 lockdown.

To investigate the air quality change during the COVID-19 pandemic, we analyzed spatiotemporal variations of six criteria pollutants in nine typical urban agglomerations in China using ground-based data and examined meteorological influences through correlation analysis and backward trajectory analysis under different responses. Concentrations of PM2.5, PM10, NO2, SO2 and CO in urban agglomerations respectively decreased by 18%-45% (30%-62%), 17%-53% (22%-39%), 47%-64% (14%-41%), 9%-34% (0%-53%) and 16%-52% (23%-56%) during Lockdown (Post-lockdown) period relative to Pre-lockdown period. PM2.5 pollution events occurred during Lockdown in Beijing-Tianjin-Hebe (BTH) and Middle and South Liaoning (MSL), and daily O3 concentration rose to grade Ⅱ standard in Post-lockdown period. Distinct from the nationwide slump of NO2 during Lockdown period, a rebound (∼40%) in Post-lockdown period was observed in Cheng-Yu (CY), Yangtze River Middle-Reach (YRMR), Yangtze River Delta (YRD) and Pearl River Delta (PRD). With slightly higher wind speed compared with 2019, the reduction of PM2.5 (51%-62%) in Post-lockdown period is more than 2019 (15%-46%) in HC (Harbin-Changchun), MSL, BTH, CP (Central Plain) and SP (Shandong-Peninsula), suggesting lockdown measures are effective to PM2.5 alleviation. Although O3 concentrations generally increased during the lockdown, its increment rate declined compared with 2019 under similar sunlight duration and temperature. Additionally, unlike HC, MSL and BTH, which suffered from additional (> 30%) air masses from surrounding areas after the lockdown, the polluted air masses reaching YRD and PRD mostly originated from the long-distance transport, highlighting the importance of joint regional governance.

Spatio-temporal variations of licensed doctor distribution in China: measuring and mapping disparities.

BACKGROUND: The licensed doctor misdistribution is one of the major challenges faced by China. However, this subject remains underexplored as spatial distribution characteristics (such as spatial clustering patterns) have not been fully mapped out by existing studies. To fill the void, this study aims to explore the spatio-temporal dynamics and spatial clustering patterns of different subtypes of licensed doctors (i.e., clinicians, traditional Chinese medicine doctors, dentists, public health doctors, general practitioners) in China. METHODS: Data on the licensed doctor quantity and population during 2012-2016 was obtained from the National Health (and Family Planning) Yearbook. Functional boxplots were used to visualize and compare the temporal trends of densities of different subtypes of licensed doctors. This study adopted two complementary spatial statistics (space-time scan statistics and Moran's I statistics) to explore the spatio-temporal dynamics and spatial clustering patterns of licensed doctor distribution in China. The former was used to explore the spatial variations in the temporal trends of licensed doctor density during 2012-2016, and the latter was adopted to explore the spatial changing patterns of licensed doctor distribution during the research period. RESULTS: The results show that the densities of almost all subtypes of licensed doctors displayed upward trends during 2012-2016, though some provincial units were left behind. Besides, spatial distribution characteristics varied across different subtypes of licensed doctors, with the low-low cluster area of general practitioners being the largest. CONCLUSIONS: The misdistribution of licensed doctors is a global problem and China is no exception. In order to achieve a balanced distribution of licensed doctors, the government is suggested to introduce a series of measures, such as deliberative policy design and effective human resource management initiatives to educate, recruit, and retain licensed doctors and prevent a brain drain of licensed doctors from disadvantaged units.

Comparison of land surface and air temperatures for quantifying summer and winter urban heat island in a snow climate city.

The urban heat island (UHI) effect is an increasingly consequential problem that confronts cities. The accurate characterization and quantification of UHI are crucial for sustainable urban development. Few UHI studies, however, compare data source, spatio-temporal variations, and indicators for the same city in parallel. This study uses Changchun, a snow climate city in China, as an example and compares five different indicators of the UHI based on land surface temperature (LST) derived from Landsat 8 TIRS and hourly air temperature (AT) collected from 41 meteorological weather stations to conduct a more comprehensive comparative study of the UHI. The results show the following. (1) The relationships between LST and AT are all statistically significant, and the surface urban heat island (SUHI) intensity characterized by the LST is considerably stronger than that of AT both in summer and winter. (2) The SUHI intensity is significantly stronger in summer (6.83 °C) than in winter (1.55 °C) based on the morning LST, whereas the UHI intensity (0.27 °C in summer and 0.40 °C in winter) that is simultaneously quantified by the AT has an opposite result. The mean whole-day and daytime UHI intensity difference, which is quantified hourly by the AT between summer and winter, is not significant. The difference between nighttime and daytime UHI intensities is evident in both summer (1.26 °C) and winter (0.76 °C). Additionally, the high temperatures for both LST and AT have a more concentrated distribution in winter than in summer. (3) The values of UHI/SUHI intensity considerably vary based on different indicators. The different choices among land covers to represent "urban" and "rural" areas would significantly affect the values of UHI/SUHI intensity. The selection of appropriate indicators and data sources to quantify the UHI remains a problem that has to be resolved in future studies.

Multivariate assessment of spatial and temporal variations in irrigation water quality in Lake Uluabat watershed of Turkey.

Irrigation water quality has important implications on salinity, ion toxicity, production cost, and crop failures. There is a need for a comprehensive analysis of spatial and temporal dynamics in parameters at a watershed scale. This information is critical for irrigation management in agricultural production. The Lake Uluabat watershed is a significant agricultural area of Turkey, which is studied using monitored water data. Multivariate assessment is performed using cluster analysis (CA), discriminant analysis (DA), principal component analysis (PCA), and factor analysis (FA) to evaluate temporal and spatial variations in water quality in the watershed. The data is processed by clustering, reducing data dimensionality, delineating indicator parameters, assessing source identification, and evaluating temporal changes and spatial patterns. The results show that the most representative discriminant parameters had more than 90.98% validity in both temporal and spatial analyses. Runoff rate (Q) and water temperature (WT) were identified in the temporal study, while spatial analysis showed bicarbonate (HCO3-), sulfate (SO42-), and boron (B3+) as indicators. Salinity, sodicity, boron hazard, and alkalinity affect both spatial and temporal water quality patterns in the watershed. It is observed that continued use of poor-quality irrigation water can adversely affect agriculture and soil health in a watershed. Spatio-temporal relationships in parameters will be useful in sustainable irrigation management and farm planning for improving crop productivity and soil health.