Dynamics of antibiotic resistance genes in the sediments of a water-diversion lake and its human exposure risk behaviour.

The dynamics and exposure risk behaviours of antibiotic resistance genes (ARGs) in the sediments of water-diversion lakes remain poorly understood. In this study, spatiotemporal investigations of ARG profiles in sediments targeting non-water (NWDP) and water diversion periods (WDP) were conducted in Luoma Lake, a typical water-diversion lake, and an innovative dynamics-based risk assessment framework was constructed to evaluate ARG exposure risks to local residents. ARGs in sediments were significantly more abundant in the WDP than in the NWDP, but there was no significant variation in their spatial distribution in either period. Moreover, the pattern of ARG dissemination in sediments was unchanged between the WDP and NWDP, with horizontal gene transfer (HGT) and vertical gene transfer (VGT) contributing to ARG dissemination in both periods. However, water diversion altered the pattern in lake water, with HGT and VGT in the NWDP but only HGT in the WDP, which were critical pathways for the dissemination of ARGs. The significantly lower ARG sediment-water partition coefficient in the WDP indicated that water diversion could shift the fate of ARGs and facilitate their aqueous partitioning. Risk assessment showed that all age groups faced a higher human exposure risk of ARGs (HERA) in the WDP than in the NWDP, with the 45-59 age group having the highest risk. Furthermore, HERA increased overall with the bacterial carrying capacity in the local environment and peaked when the carrying capacity reached three (NWDP) or four (WDP) orders of magnitude higher than the observed bacterial population. HGT and VGT promoted, whereas ODF covering gene mutation and loss mainly reduced HERA in the lake. As the carrying capacity increased, the relative contribution of ODF to HERA remained relatively stable, whereas the dominant mechanism of HERA development shifted from HGT to VGT.

[Analysis of the Spatiotemporal Distribution of Algal Blooms and Its Driving Factors in Chaohu Lake Based on Multi-source Datasets].

Eutrophication and harmful algae blooms are one of the common ecological and environmental problems faced by freshwater lakes all over the world. As a typical inland freshwater lake, Chaohu Lake exhibits a high level of eutrophication and algae blooms year-round and shows a spatiotemporal difference in different regions of the lake. In order to understand the basic regularity of the development and outbreak of algal blooms in Chaohu Lake, the data from the comprehensive water observation platform and remote sensing were integrated to obtain the spatiotemporal distribution of algal blooms from 2015 to 2020. Then, an evaluation model based on Boosted Regression Trees (BRT) was constructed to quantitatively assess the importance and interactions of various environmental factors on algal blooms at different stages. The results indicated that:① The occurrence of algal blooms in Chaohu Lake exhibited significant seasonal variations, with the cyanobacteria beginning to recover in spring and bring about a light degree of algal blooms in the western and coastal areas of Chaohu Lake. The density of cyanobacteria reached its maximum in summer and autumn, accompanied by moderate and severe degrees of algal bloom outbreaks. ② During the non-outbreak period, the variation in the cyanobacteria density was greatly affected by physical and chemical factors, which explained 80.3% of the variance in the change in cyanobacteria density. The high concentrations of dissolved oxygen content in the water column and the weak alkalinity (7.2-7.6) and appropriate water temperature (about 3℃) provided a favorable environmental condition for the breeding and growth of cyanobacteria. In addition, the onset of algal blooms was closely related to the air temperature steadily passing through the threshold. According to the statistics, the date of first outbreak of algal blooms in Chaohu Lake was 11 days or so after the air temperature steadily remained above 7℃. ③ During the outbreak period, the occurrence of algal blooms was influenced by the combination of cyanobacterial biomass and meteorological conditions such as temperature, wind speed, and sunshine duration. The cumulative contribution ratio of the four factors was as high as 95%, and each factor had an optimal interval conductive to the outbreak of algal blooms. Furthermore, the results of multi-factor interaction analysis indicated a larger probability of the outbreak of algal blooms in Chaohu Lake under the combined effect of high cyanobacteria density, suitable temperature, and the breeze. This study analyzed and revealed the spatiotemporal characteristics and the dominant influencing factors of algal blooms in Chaohu Lake at different stages, which could provide the scientific basis for the prediction, early warning, and disposal of algal blooms under the context of climate change.

Investigating the spatiotemporal dynamics of apple tree phyllosphere bacterial and fungal communities across cultivars in orchards.

The phyllosphere, a reservoir of diverse microbial life associated with plant health, harbors microbial communities that are subject to various complex ecological processes acting at multiple scales. In this study, we investigated the determinants of the spatiotemporal variation in bacterial and fungal communities within the apple tree phyllosphere, employing 16S and ITS amplicon sequencing. Our research assessed the impact of key factors-plant compartment, site, time, and cultivar-on the composition and diversity of leaf and flower microbial communities. Our analyses, based on samples collected from three cultivars in three orchards in 2022, revealed that site and time are the strongest drivers of apple tree phyllosphere microbial communities. Conversely, plant compartment and cultivar exhibited minor roles in explaining community composition and diversity. Predominantly, bacterial communities comprised Hymenobacter (25%) and Sphingomonas (10%), while the most relatively abundant fungal genera included Aureobasidium (27%) and Sporobolomyces (10%). Additionally, our results show a gradual decrease in alpha-diversity throughout the growth season. These findings emphasize the necessity to consider local microbial ecology dynamics in orchards, especially as many groups worldwide aim for the development of biocontrol strategies (e.g., by manipulating plant-microbe interactions). More research is needed to improve our understanding of the determinants of time and site-specific disparities within apple tree phyllosphere microbial communities across multiple years, locations, and cultivars.

Impacts of ship emissions and sea-land breeze on urban air quality using chemical characterization, source contribution and dispersion model simulation of PM2.5 at Asian seaport.

Fine particulate matter (PM2.5) emitted from marine transportation, bulk materials handling at the docks, and dust dispersion has garnered increased attention, particularly in the interface between port and urban areas. This study explored the inter-transport of PM2.5 between Kaohsiung Harbor and neighboring Metro Kaohsiung. Chemical analyses of PM2.5 samples from four sites include water-soluble ions, metallic elements, carbons, anhydrosugars, and organic acids to establish PM2.5's chemical fingerprints. The CALPUFF air dispersion model is employed to simulate the spatiotemporal distribution of PM2.5 in Kaohsiung Harbor and adjacent urban areas. A clear seasonal and diurnal variation of PM2.5 concentrations and chemical composition was observed in both harbor and urban areas. The high correlation of nighttime PM2.5 levels between the port and urban areas suggests inter-transport phenomena. Sea salt spray, ship emissions, secondary aerosols, and heavy fuel-oil boilers exhibit higher levels in the port area than in the urban area. In Metro Kaohsiung, mobile sources, fugitive dust, and waste incinerators emerge as major PM2.5 contributors. Furthermore, sea breeze significantly influences PM2.5 dispersion from Kaohsiung Harbor to Metro Kaohsiung, particularly in the afternoon. The average contribution of PM2.5 from ships' main engines in Kaohsiung Harbor ranges from 2.9% to 5.3%, while auxiliary engines contribute 3.8%-8.3% of PM2.5 in Metro Kaohsiung.

Spatiotemporal variation of 6PPD and 6PPDQ in dust and soil from e-waste recycling areas.

N-(1,3-dimethylbutyl)-N'-phenyl-p-phenylenediamine (6PPD) and its derivative 6PPDQ have been detected in various environmental media, with harmful consequences for both ecosystems and biological health. However, the distribution of 6PPD and 6PPDQ in areas around e-waste recycling areas is currently unknown. We collected soil and dust samples from areas around a traditional e-waste recycling zone, an emerging recycling park, and a reference area. Higher levels of 6PPD were found in dust from residential areas around the traditional e-waste recycling zone compared to the reference area (median: 108.99 versus 33.57 ng/g, P < 0.01). Lower levels of 6PPDQ were detected in dust samples from around the emerging e-waste recycling parks compared to traditional e-waste recycling zones (median: 15.40 versus 46.37 ng/g, P < 0.05). The median concentrations of 6PPD and 6PPDQ were higher in the dust samples than in the soil samples (P < 0.001). The concentrations of 6PPD and 6PPDQ in the dust and soil varied seasonally, with the highest total concentrations occurring in the winter. Results from a multiple linear regression analysis indicate that 6PPDQ is negatively correlated with temperature and positively correlated with 6PPD, O3, and radiation. This study confirms that e-waste is a potential contributor to 6PPD and 6PPDQ. In residential areas, 6PPD and 6PPDQ are more likely to accumulate in dust than in soil. The emerging e-waste recycling parks have greatly improved the local 6PPDQ pollution situation. Further studies are necessary to understand the distribution of newly found substances in various settings.

Neonicotinoid insecticides in waters of the northern Jiangsu segment of the Beijing-Hangzhou Grand Canal: Environmental and health implications.

Neonicotinoid (NEO) insecticides have been frequently detected in natural aquatic environments. Nevertheless, the distribution of NEOs in artificial environments is not clear. The Beijing-Hangzhou Grand Canal is the longest canal in the world. The northern Jiangsu segment of the Grand Canal was selected to study the spatiotemporal variation and source of eight NEOs in the canal water and assess their ecological and health risks. The total NEO concentration in the canal water was 12-289 ng L-1 in the dry season and 18-373 ng L-1 in the wet season, which were within the concentration range in other 11 natural rivers worldwide. The average total NEO concentrations were not statistically different between the seasons; only the concentrations of imidaclothiz, thiacloprid (THI), acetamiprid, and dinotefuran were different. At city scale, the total NEO concentration in the dry season showed a decreasing trend along the water flow from Xuzhou City to Yangzhou City. The total NEO concentrations were found to be positively correlated with the sown area of farm crops and the rural labour force, indicating the agricultural influence on the spatial distribution of NEO concentrations. In the wet season, relatively high NEO concentrations were distributed in downstream sites under the influence of artificial regulation. The primary contributor to the NEO inputs into the canal was the nonpoint source in the dry and wet seasons, with a relative contribution of 68 %. THI, imidacloprid, clothianidin and thiamethoxan would produce chronic ecological risks in both seasons. Further consideration needs to be given to the above four NEOs and NEO mixtures. The human health risks that NEOs posed by drinking water were assessed based on the chronic daily intake (CDI). The maximum CDI for adults and children was lower than the reference doses. This suggested public health would not be at risk from canal water consumption.

Gender disparities in summer outdoor heat risk across China: Findings from a national county-level assessment during 1991-2020.

Increasing anthropogenic global warming has emerged as a significant challenge to human health in China, as extreme heat hazards increasingly threaten outdoor-exposed populations. Differences in thermal comfort, outdoor activity duration, and social vulnerability between females and males may exacerbate gender inequalities in heat-related health risks, which have been overlooked by previous studies. Here, we combine three heat hazards and outdoor activity duration to identify the spatiotemporal variation in gender-specific heat risk in China during 1991-2020. We found that females' heat risk tends to be higher than that of males. Gender disparities in heat risk decrease in southern regions, while those in northern regions remain severe. Males are prone to overheating in highly urbanized areas, while females in low urbanized areas. Males' overheating risk is mainly attributed to population clustering associated with prolonged outdoor activity time and skewed social resource allocation. In contrast, females' overheating risk is primarily affected by social inequalities. Our findings suggest that China needs to further diminish gender disparities and accelerate climate adaptation planning.

Insight into the Role of NH3/NH4+ and NOx/NO3- in the Formation of Nitrogen-Containing Brown Carbon in Chinese Megacities.

Particulate brown carbon (BrC) plays a crucial role in the global radiative balance due to its ability to absorb light. However, the effect of molecular formation on the light absorption properties of BrC remains poorly understood. In this study, atmospheric BrC samples collected from six Chinese megacities in winter and summer were characterized through ultrahigh-performance liquid chromatography coupled with Orbitrap mass spectrometry (UHPLC-Orbitrap MS) and light absorption measurements. The average values of BrC light absorption coefficient at a wavelength of 365 nm (babs365) in winter were approximately 4.0 times higher than those in summer. Nitrogen-containing organic molecules (CHNO) were identified as critical components of light-absorbing substances in both seasons, underscoring the importance of N-addition in BrC. These nitrogen-containing BrC chromophores were more closely related to nitro-containing compounds originating from biomass burning and nitrogen oxides (NOx)/nitrate (NO3-) reactions in winter. In summer, they were related to reduced N-containing compounds formed in ammonia (NH3)/ammonium (NH4+) reactions. The NH3/NH4+-mediated reactions contributed more to secondary BrC in summer than winter, particularly in southern cities. Compared with winter, the higher O/Cw, lower molecule conjugation indicator (double bond equivalent, DBE), and reduced BrC babs365 in summer suggest a possible bleaching mechanism during the oxidation process. These findings strengthen the connection between molecular composition and the light-absorbing properties of BrC, providing insights into the formation mechanisms of BrC chromophores across northern and southern Chinese cities in different seasons.

Monthly average air pollution models using geographically weighted regression in Europe from 2000 to 2019.

Detailed spatial models of monthly air pollution levels at a very fine spatial resolution (25 m) can help facilitate studies to explore critical time-windows of exposure at intermediate term. Seasonal changes in air pollution may affect both levels and spatial patterns of air pollution across Europe. We built Europe-wide land-use regression (LUR) models to estimate monthly concentrations of regulated air pollutants (NO2, O3, PM10 and PM2.5) between 2000 and 2019. Monthly average concentrations were collected from routine monitoring stations. Including both monthly-fixed and -varying spatial variables, we used supervised linear regression (SLR) to select predictors and geographically weighted regression (GWR) to estimate spatially-varying regression coefficients for each month. Model performance was assessed with 5-fold cross-validation (CV). We also compared the performance of the monthly LUR models with monthly adjusted concentrations. Results revealed significant monthly variations in both estimates and model structure, particularly for O3, PM10, and PM2.5. The 5-fold CV showed generally good performance of the monthly GWR models across months and years (5-fold CV R2: 0.31-0.66 for NO2, 0.4-0.79 for O3, 0.4-0.78 for PM10, 0.46-0.87 for PM2.5). Monthly GWR models slightly outperformed monthly-adjusted models. Correlations between monthly GWR model were generally moderate to high (Pearson correlation >0.6). In conclusion, we are the first to develop robust monthly LUR models for air pollution in Europe. These monthly LUR models, at a 25 m spatial resolution, enhance epidemiologists to better characterize Europe-wide intermediate-term health effects related to air pollution, facilitating investigations into critical exposure time windows in birth cohort studies.

Fine-scale spatiotemporal variations in bacterial community diversity in agricultural pond water.

Microbial communities in surface waters are affected by environmental conditions and can influence changes in water quality. To explore the hypothesis that the microbiome in agricultural waters associates with spatiotemporal variations in overall water quality and, in turn, has implications for resource monitoring and management, we characterized the relationships between the microbiota and physicochemical properties in a model irrigation pond as a factor of sampling time (i.e., 9:00, 12:00, 15:00) and location within the pond (i.e., bank vs. interior sites and cross-sectional depths at 0, 1, and 2 m). The microbial communities, which were defined by 16S rRNA gene sequencing analysis, significantly varied based on all sampling factors (PERMANOVA P < 0.05 for each). While the relative abundances of dominant phyla (e.g., Proteobacteria and Bacteroidetes) were relatively stable throughout the pond, subtle yet significant increases in α-diversity were observed as the day progressed (ANOVA P < 0.001). Key water quality properties that also increased between the morning and afternoon (i.e., pH, dissolved oxygen, and temperature) positively associated with relative abundances of Cyanobacteria, though were inversely proportional to Verrucomicrobia. These properties, among additional parameters such as bioavailable nutrients (e.g., NH3, NO3, PO4), chlorophyll, phycocyanin, conductivity, and colored dissolved organic matter, exhibited significant relationships with relative abundances of various bacterial genera as well. Further investigation of the microbiota in underlying sediments revealed significant differences between the bank and interior sites of the pond (P < 0.05 for α- and ß-diversity). Overall, our findings emphasize the importance of accounting for time of day and water sampling location and depth when surveying the microbiomes of irrigation ponds and other small freshwater sources.

Applicability of evaluation metrics/schemes for human health burden attributable to regional ozone pollution: A case study in the Guangdong-Hong Kong-Macao Greater Bay Area (GBA), South China.

This is a study to identify the applicable/preferable short- and long-term metrics/schemes to evaluate the premature mortality attributable to the ozone pollution in the Guangdong-Hong Kong-Macao Greater Bay Area (GBA), one of the most representative populous ozone pollution regions in China, by comprehensively accounting the uncertainty sources. The discrepancy between the observation and the CAQRA reanalysis datasets (2013-2019) was investigated in terms of the concentration variation pattern, which determines the exposure metric change. A set of domestic short-term C-R coefficients for the all-age population were integrated using the meta-analysis respectively corresponding to the metrics of MDA1, MDA8, and Daily average. The dataset-based deviations of the short-term attributable factors (AFs) and their corresponding premature mortalities were respectively about 16.9 ± 13.3 % and <5 % based on MDA8, much smaller than other two metrics; and the MDA8-based evaluation results were the most sensitive to the deteriorative ozone pollution, with the maximum upward trends of 0.095-0.129 %/year. Accordingly, MDA8 was recognized as the most applicable short-term metric. For the long-term exposure, the domestic summer metric SMDA8 could not exactly represent the peak-season ozone maximum level in the GBA, with the deviation from 6MMDA8 as much as 30 %. By considering the ability of metric to represent the peak-season ozone, the relatively smaller dataset-based discrepancies of AFs (6MMDA8-WHO2021: 23.3 ± 16.9 %, AMDA8-T2016: 20.7 ± 15.8 %) and the attributable premature mortalities (6MMDA8-WHO2021: 5 %, AMDA8-T2016: 8 %), and the higher sensitivity of the evaluation results to the deteriorative ozone pollution (6MMDA8-WHO2021: 0.13 %;year, p = 0.01; AMDA8-T2016: 0.15 %/year, p = 0.03), the schemes of 6MMDA8-WHO2021 and AMDA8-T2016 were recognized relatively more preferable for the adult (≥25-year) long-term evaluation. Based on the recognized metric/schemes, the central and the eastern PRE areas of higher NO2 level in the GBA were experiencing the highest health burdens from 2013 to 2019.

Understanding the spatial and seasonal variation of the ground-level ozone in Southeast China with an interpretable machine learning and multi-source remote sensing.

Ground-level ozone (O3) pollution poses significant threats to both human health and air quality. This study uses ground observations and satellite retrievals to explore the spatiotemporal characteristics of ground-level O3 in Zhejiang Province, China. We created data-driven machine learning models that include meteorological, geographical and atmospheric parameters from multi-source remote sensing products, achieving good performance (Pearson's r of 0.81) in explaining regional O3 dynamics. Analyses revealed the crucial roles of temperature, relative humidity, total column O3, and the distributions and interactions of precursor (volatile organic compounds and nitrogen oxides) in driving the varied O3 patterns observed in Zhejiang. Furthermore, the interpretable modeling quantified multifactor interactions that sustain high O3 levels in spring and autumn, suppress O3 levels in summer, and inhibit O3 formation in winter. This work demonstrates the value of a combined approach using satellite and machine learning as an effective novel tool for regional air quality assessment and control.

Spatiotemporal variation and inter-transport of atmospheric speciated mercury between Kaohsiung Harbor and neighboring urban areas.

This study investigated the spatiotemporal variation, gas-particle partition, and source resolution of atmospheric speciation mercury (ASM) in Kaohsiung Harbor and neighboring Metro Kaohsiung. Four sampling sites were selected to determine the pollution characteristics and inter-transport of ASM between the port and urban areas. The yearly average GEM, GOM, and PBM concentrations were 7.13 ± 2.2 ng/m3, 331 ± 190 pg/m3, and 532 ± 301 pg/m3, respectively. Notably, GEM emerged as the predominant ASM species (85-94%), primarily originating from anthropogenic emissions from the harbor area and nearby industrial complex. The study revealed a distinct seasonal variation in ASM concentrations in the Kaohsiung Area in the following order: winter > fall > spring > summer. Concerning spatial distribution, ASM concentrations in the port areas were generally higher than those in the urban areas. This disparity was chiefly attributed to the influence of the prevailing winds, local sources, and atmospheric dispersion. Backward trajectory simulation revealed that polluted air masses blown from the northeast in winter and spring, moving along the western in-land part of Taiwan Island, were likely influenced by local sources and long-range transport (LRT). In summer, air pollutants originating from the south were likely transported from the coastal industrial sources. During fall, air masses blown from the western offshore waters transported air pollutants from Kaohsiung Harbor to neighboring Metro Kaohsiung. The results obtained from principle component analysis (PCA) indicated that primary sources in the port areas included ship emissions, vehicular exhausts, and nearby industrial complex, which align with the primary source factors identified by positive matrix factorization (PMF), which were mobile sources and coal-fired industrial boilers. Meanwhile, mobile sources and sulfur-containing fuel/waste combustion were identified as the primary sources in the urban areas.

Bioaerosols in the atmosphere: A comprehensive review on detection methods, concentration and influencing factors.

In the past few decades, especially since the outbreak of the coronavirus disease (COVID-19), the effects of atmospheric bioaerosols on human health, the environment, and climate have received great attention. To evaluate the impacts of bioaerosols quantitatively, it is crucial to determine the types of bioaerosols in the atmosphere and their spatial-temporal distribution. We provide a concise summary of the online and offline observation strategies employed by the global research community to sample and analyze atmospheric bioaerosols. In addition, the quantitative distribution of bioaerosols is described by considering the atmospheric bioaerosols concentrations at various time scales (daily and seasonal changes, for example), under various weather, and different underlying surfaces. Finally, a comprehensive summary of the reasons for the spatiotemporal distribution of bioaerosols is discussed, including differences in emission sources, the impact process of meteorological factors and environmental factors. This review of information on the latest research progress contributes to the emergence of further observation strategies that determine the quantitative dynamics of public health and ecological effects of bioaerosols.

Global prediction of gross primary productivity under future climate change.

The ecosystem gross primary productivity (GPP) is crucial to land-atmosphere carbon exchanges, and changes in global GPP as well as its influencing factors have been well studied in recent years. However, identifying the spatio-temporal variations of global GPP under future climate changes is still a challenging issue. This study aims to develop data-driven approach for predicting the global GPP as well as its monthly and annual variations up to the year 2100 under changing climate. Specifically, Catboost was employed to examine the potential relationship between the GPP and environmental factors, with climate variables, CO2 concentration and terrain attributes being selected as environmental factors. The predicted monthly and annual GPP from Coupled Model Intercomparison Project phase 6 (CMIP6) under future SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5 scenarios were analyzed. The results indicate that the global GPP is predicted to increase under the future climate change in the 21st century. The annual GPP is expected to be 115.122 Pg C, 116.537 Pg C, 117.626 Pg C, and 120.097 Pg C in 2100 under four future scenarios, and the predicted monthly GPP shows seasonal difference. Meanwhile, GPP tends to increase in the northern mid-high latitude regions and decrease in the equatorial regions. For the climate zones form Köppen-Geiger classification, the arid, cold, and polar zones present increased GPP, while GPP in the tropical zone will decrease in the future. Moreover, the high importance of climate variables in GPP prediction illustrates that the future climate change is the main driver of the global GPP dynamics. This study provides a basis for predicting how global GPP responds to future climate change in the coming decades, which contribute to understanding the interactions between vegetation and climate.

Quantifying multi-dimensional services of water ecosystems and breakpoint-based spatial radiation of typical regulating services considering the hierarchical clustering-based classification.

This study proposes a set of water ecosystem services (WES) research system, including classification, benefit quantification and spatial radiation effect, with the goal of promoting harmonious coexistence between humans and nature, as well as providing a theoretical foundation for optimizing water resources management. Hierarchical cluster analysis was applied to categorize WES taking in to account the four nature constraints of product nature, energy flow relationships, circularity, and human social utility. A multi-dimensional benefit quantification methodology system for WES was constructed by combining the emergy theory with multidisciplinary methods of ecology, economics, and sociology. Based on the theories of spatial autocorrelation and breaking point, we investigated the spatial radiation effects of typical services in the cyclic regulation category. The proposed methodology has been applied to Luoyang, China. The results show that the Resource Provisioning (RP) and Cultural Addition (CA) services change greatly over time, and drive the overall WES to increase and then decrease. The spatial and temporal distribution of water resources is uneven, with WES being slightly better in the southern region than the northern region. Additionally, spatial radiation effects of typical regulating services are most prominent in S County. This finding suggests the establishment of scientific and rational intra-basin or inter-basin water management systems to expand the beneficial impacts of water-rich areas on neighboring regions.

Spatiotemporal characteristic analysis of PM2.5 in central China and modeling of driving factors based on MGWR: a case study of Henan Province.

Since the start of the twenty-first century, China's economy has grown at a high or moderate rate, and air pollution has become increasingly severe. The study was conducted using data from remote sensing observations between 1998 and 2019, employing the standard deviation ellipse model and spatial autocorrelation analysis, to explore the spatiotemporal distribution characteristics of PM2.5 in Henan Province. Additionally, a multiscale geographically weighted regression model (MGWR) was applied to explore the impact of 12 driving factors (e.g., mean surface temperature and CO2 emissions) on PM2.5 concentration. The research revealed that (1) Over a period of 22 years, the yearly mean PM2.5 concentrations in Henan Province demonstrated a trend resembling the shape of the letter "M", and the general trend observed in Henan Province demonstrated that the spatial center of gravity of PM2.5 concentrations shifted toward the north. (2) Distinct spatial clustering patterns of PM2.5 were observed in Henan Province, with the northern region showing a primary concentration of spatial hot spots, while the western and southern areas were predominantly characterized as cold spots. (3) MGWR is more effective than GWR in unveiling the spatial heterogeneity of influencing factors at various scales, thereby making it a more appropriate approach for investigating the driving mechanisms behind PM2.5 concentration. (4) The results acquired from the MGWR model indicate that there are varying degrees of spatial heterogeneity in the effects of various factors on PM2.5 concentration. To summarize the above conclusions, the management of the atmospheric environment in Henan Province still has a long way to go, and the formulation of relevant policies should be adapted to local conditions, taking into account the spatial scale effect of the impact of different influencing factors on PM2.5.

Epidemiological analysis of anaplasmosis in cattle from Khyber Pakhtunkhwa, Pakistan.

Background and Aim: Anaplasmosis, a tick-borne disease affecting livestock caused by the bacteria Anaplasma, poses a global concern. This study aimed to estimate the prevalence, spatiotemporal variation, and associated risk factors of anaplasmosis in cattle from the Bannu and Lakki Marwat districts of Khyber Pakhtunkhwa, Pakistan. Materials and Methods: This study used 197 cattle exhibiting clinical symptoms of anaplasmosis in natural settings. Microscopic examination was used to estimate the prevalence. Potential risk factors, such as sampling regions and months, gender, breed, and age were studied. Results: The study revealed an overall anaplasmosis prevalence of 19.79%. Bannu district exhibited a higher occurrence at 22.10%, compared to Lakki Marwat district at 17.64%. Young cattle (<2 years) demonstrated a notably higher incidence of anaplasmosis (26.78%) compared to adults (>5 years), which had a prevalence of 12.35% (p < 0.05). Female cattle (22.36%) were more susceptible than male cattle (11.11%). Prevalence peaked in June (45.71%) and was lowest in February (3.57%). Crossbred cattle had a higher prevalence (23.52%) than purebred cattle (11.47%). Conclusion: Anaplasmosis can be effectively controlled using a comprehensive approach encompassing selective breeding for resilience, targeted care of young calves and females, effective tick control during warmer months, consistent use of insecticides, and proactive risk factor management. Raising awareness among farmers through diverse channels, including media, is pivotal to bolster tick-borne disease management strategies.

Socioeconomic and seasonal effects on spatiotemporal trends in estrogen occurrence and ecological risk within a river across low-urbanized and high-husbandry landscapes.

Estrogen pollution is a persistent issue in rivers. This study investigated the occurrence, spatiotemporal variation mechanisms, sources, and ecological risks of estrone (E1), 17ß-estradiol (E2), estriol (E3), 17α-ethinylestradiol (EE2), diethylstilbestrol (DES), and bisphenol-A (BPA) in the waters of the Zijiang River, a tributary of the middle Yangtze River. The results revealed elevated detection frequencies and estrogen concentrations in the dry season compared to the wet season, mainly due to the precipitation dilution effect. Total estrogen concentration ranged from 21.2 to 97.5 ng/L in the dry season, which was significantly correlated to spatial distributions of animal husbandry and population. Among the estrogens studied in the river, E2, BPA, and EE2 were predominant. The collective sources of E1, E2, E3, and EE2 were traced back to human and husbandry excrement, whereas BPA emitted from daily life products, contributing to 55.5% and 42.7% of the total estrogen concentration, respectively. Particularly, the average and median E1, E2, and EE2 concentrations in the river exceeded the environmental quality standards of the European Union. The total estrogenic activity dominated by EE2 exceeded the 1 ng E2/L threshold, with levels exceeding 10 ng E2/L during the dry season. The risk quotients exhibited a high ecological risk of E1 and EE2 to fish and a moderate to high ecological risk of E1 to crustaceans, EE2 to mollusks, and E2 to fish. Therefore, E1, E2, and EE2 pollution of the river may lead to both high estrogenic potency and moderate or high ecological risk; thus, they should be considered priority pollutants in the river. These results yield valuable insights into the spatiotemporal change mechanisms, sources, and ecological risks of estrogens in river water of low-urbanization and rural watersheds.

Low-Cost Particulate Matter Sensors for Monitoring Residential Wood Burning.

Conventional monitoring systems for air quality, such as reference stations, provide reliable pollution data in urban settings but only at relatively low spatial density. This study explores the potential of low-cost sensor systems (LCSs) deployed at homes of residents to enhance the monitoring of urban air pollution caused by residential wood burning. We established a network of 28 Airly (Airly-GSM-1, SP. Z o.o., Poland) LCSs in Kristiansand, Norway, over two winters (2021-2022). To assess performance, a gravimetric Kleinfiltergerät measured the fine particle mass concentration (PM2.5) in the garden of one participant's house for 4 weeks. Results showed a sensor-to-reference correlation equal to 0.86 for raw PM2.5 measurements at daily resolution (bias/RMSE: 9.45/11.65 µg m-3). High-resolution air quality maps at a 100 m resolution were produced by combining the output of an air quality model (uEMEP) using data assimilation techniques with the network data that were corrected and calibrated by using a proposed five-step network data processing scheme. Leave-one-out cross-validation demonstrated that data assimilation reduced the model's RMSE, MAE, and bias by 44-56, 38-48, and 41-52%, respectively.