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Interception loss (IL) is an important process in the hydrological cycle within semi-arid forest ecosystems, directly affecting the amount of effective rainfall. However, the factors influencing IL during individual rainfall events remain to be quantified. This study collected rainfall, vegetation, and interception data during the 2022 and 2023 growing seasons in a typical black locust forest within the Zhifanggou watershed. It employed the Random Forest Regression (RFR) and back-propagation neural network (BPNN) methods to quantitatively evaluate the contribution rates of various factors to the IL and interception loss percentage (ILP). The IL among the 48 effective rainfall events was 172.05 mm, accounting for 19.54% of the rainfall amount. IL and ILP increased as the distance from the trunk decreased. During all rainfall events, both IL and ILP were significantly negatively correlated with the leaf area index (LAI) and canopy cover (CC); IL is significantly positively correlated with total rainfall (TR) and rainfall intensity (RI), while ILP is significantly negatively correlated with TR, RI, and rainfall duration (RD). The BPNN and RFR results indicated that rainfall, canopy, and tree characteristics contributed 43.06%, 44.79%, and 12.15% to IL, respectively, and 57.27%, 34.09%, and 8.63% to ILP, respectively. TR, CC, and LAI represented the primary influencing factors. Rainfall and canopy characteristics were the main factors affecting IL (ILP). As rainfall event magnitude increases, canopy contributions to IL and ILP decrease. In semi-arid areas, managing forest canopies to control IL helps address water imbalances in ecosystems.

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Real-time assessment of ecological environment quality in arid and semi-arid regions is crucial for the sustainable development of ecological environments in China. In this study, we constructed a topsoil remote sensing ecological index (TRSEI) by coupling five indicators, greenness, wetness, dryness, topsoil grain size, and heat, with the Google Earth Engine (GEE). With the index, we evaluated the ecological environment quality of Wuchuan County from 1990 to 2020, and examined the spatio-temporal variations of ecological environment quality and its driving factors by using univariate linear regression, multiple regression residual analysis, and Hurst index. Results showed that the first principal component of the TRSEI in the study area contributed over 70%, with a mean eigenvalue of 0.148, indicating the effective integration of various ecological indicators by TRSEI. The topsoil grain size index was essential for the assessment of ecological environment quality in arid and semi-arid regions. From 1990 to 2020, the fluctuation range of TRSEI in the study area was between 0.289 and 0.458, showing an overall slight deterioration trend. The ecological environment quality of cropland and de-farming region had improved, with the improved area accounting for 47.9% of the total area. The grassland, barren land, and construction land areas had deteriorated, with the deteriorated area accounting for 52.1% of the total area. In the future, 36.9% of the regions would experience continuous improvement in ecological environment quality, while 41.4% might continue to dete-riorate. Human activities were the primary driving factor for the changes in ecological environment quality in arid and semi-arid regions, accounting for 88.6% of the total area. Climate change also had a significant impact, accounting for 11.4% of the total area. The TRSEI could effectively assess the ecological environment quality of arid and semi-arid regions, providing a scientific basis for ecological conservation and construction in these areas.

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Soil organic carbon and nitrogen play pivotal roles as indicators of soil quality and ecological functioning in wetlands. The escalating impact of human activities and climate change has led to a severe degradation of wetland soils, particularly in semi-arid regions. However, an understanding of the factors governing the dynamics of total soil organic carbon (TSOC) and total soil nitrogen (TSN) in semi-arid areas remains elusive, impeding a comprehensive understanding of wetland ecological functions. The present study investigated variations in TSOC and TSN content as well as vegetation and soil physicochemical properties under five different land management practices (mowed wetlands, mowed and slightly grazed wetlands, moderately grazed wetlands, heavily grazed wetlands, and natural wetlands unaffected by human interference) in the semi-arid Songnen Plain region of China. The results revealed significant decreases in TSOC and TSN content within managed wetlands compared to natural wetlands. Moreover, positive correlations were observed between pairs of SOC-TN or their storage values for SOC (TSOC)-TN (TSN). Furthermore, TSOC and TSN exhibited significant positive associations with aboveground and belowground biomass levels, stem C:N, stem C:P, soil C:P, and soil N:P. Additionally, redundancy analysis indicated that species diversity accounted for 37.4% of the variations in TSOC-TSN while belowground biomass accounted for 8.5% of the variations. Furthermore, nutrient content within stems (particularly N content and C:P) contributed to a 37.2% variation in TSOC and TSN whereas root nutrient content (especially N:P, C:N, and C:P) contributed to a 15.3% variation. Soil C:P, C:N, and total phosphorous (TP) content accounted for 65.7%, 9.6%, and 7.5% of variations of TSOC and TSN, respectively. Besides, variation partitioning analysis revealed that plant community characteristics, community nutrient content, and soil physicochemical properties collectively influenced the dynamics of TSOC and TSN. Among these factors, soil physicochemical properties emerged as the primary drivers of carbon and nitrogen dynamics in degraded wetlands in semi-arid regions. The impact on TSN was more pronounced than that of TSOC. This study provides valuable insights for understanding the processes and mechanisms underlying carbon and nitrogen accumulation in degraded wetlands, facilitating the development of regionally adaptive management plans under different management practices.

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In the sparse studies for multiple pathway exposure, attention has predominantly been directed towards developed regions, thereby overlooking the exposure level and health outcome for the inhabitants of the semi-arid regions in northwest China. However, cities within these regions grapple with myriad challenges, encompassing insufficient sanitation infrastructure and outdated heating. In this study, we analyzed the characteristics and sources of polycyclic aromatic hydrocarbons (PAHs) pollution in PM2.5, water, diet, and dust during different periods in Lanzhou, and estimated corresponding carcinogenic health risk through inhalation, ingestion, and dermal absorption. Our observations revealed the concentrations of PAHs in PM2.5, food, soil, and water are 200.11 ng m-3, 8.67 mg kg-1, 3.91 mg kg-1, and 14.5 ng L-1, respectively, indicating that the Lanzhou area was seriously polluted. Lifetime incremental cancer risk (ILCR) showed a heightened cancer risk to men compared to women, to the younger than the elderly, and during heating period as opposed to non-heating period. Notably, the inhalation was the primary route of PAHs exposure and the risk of exposure by inhalation cannot be ignored. The total environmental exposure assessment of PAHs can achieve accurate prevention and control of PAHs environmental exposure according to local conditions and targets.

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Alien invasive plants have been found in the semi-arid region of Northeast China for a long time, but the overall invasion situation is rarely reported. In this study, we established a database of alien invasive plants in the semi-arid area of Northeast China through field investigation, specimen collection, research of specimen online information platform and literature. The results showed that there were 34 species of alien invasive plants belonging to 26 genera and 10 families in the semi-arid area of Northeast China, among which the Composite family had the largest number of richness, with 9 genera (34.6%) and 11 species (32.4%). There were 15 species (44.1%) in 11 genera (42.3%) of Legumes, Solanaceae and Gramineae. In all the alien invasive plants, 33 species were herbaceous plants, being overwhelmingly dominant (97.1%). There were both 7 species of countrywide invasive plants with invasive grade 1 and 2, each accounting for 20.6% of the total. The number of species with invasive grade 4 was the largest, 17 species, accounting for 50% of the total. The invasive plants originated in North America and Europe was the most, accounting for 64.7%, while those from South America, Asia and Africa accounted for 35.3%. Totally, 44.1% of all the invasive alien plants were intentionally introduced, while 55.9% were unintentionally introduced. In the semi-arid area of Northeast China, 81.3% of the counties (cities) had the distribution of alien invasive plants, and the invasion situation was very serious.

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This study aimed to assess the impact of adding forage cactus as an additive to the production of corn silage without the cob on the performance of feedlot sheep and subsequent silage losses. The experimental design was completely randomized, consisting of three treatments: corn silage without cob; 0% = 100% corn plant without the cob; 10% = 90% corn plant without cob + 10% forage cactus; 20% = 80% corn plant without cob + 20% forage cactus. Significant effects were observed for dry matter intake (P = 0.0201), organic matter (P = 0.0152), ether extract (P = 0.0001), non-fiber carbohydrates (P = 0.0007). Notably, nutrient digestibility showed significant differences in organic matter (P = 0.0187), ether extract (P = 0.0095), neutral detergent fiber (P = 0.0005), non-fiber carbohydrates (P = 0.0001), and metabolizable energy (P = 0.0001). Performance variables, including total weight gain (P = 0.0148), average daily weight gain (P = 0.0148), feeding efficiency, and rumination efficiency of dry matter (P = 0.0113), also exhibited significant effects. Consequently, it is recommended to include 20% forage cactus in corn silage, which, based on natural matter, helps meet animals' water needs through feed. This inclusion is especially vital in semi-arid regions and aids in reducing silage losses during post-opening silo disposal.

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Global emission reductions still must address winter fine particulate matter (PM2.5) pollution in urban basins with enclosed terrains and frequent cold air pool (CAP) events when the temperatures within the basin are colder than above it. The effects of urban basin aerosol-boundary layer interactions on PM2.5 pollution during CAP events remain unclear. Intensive boundary layer observations in January 2021 and numerical models were used to investigate this issue in the semi-arid urban Lanzhou Basin of China. The results showed that CAPs formed because of the synoptic weather system that exacerbated the warming over the basin. The CAPs in this experiment were characterized by stronger temperature inversion (TI) layers in the vertical direction and lower relative humidity, lower wind speed, and weaker turbulence at the bottom of the basin compared to other conditions. The strong TI layers below the top of the basin inhibited the vertical dispersion of pollutants in the basin and concentrated the PM2.5 within a height of 0.3 km from the bottom of the basin. During CAP events, the proportion of elemental carbon in PM2.5 increased, whereas that of secondary inorganic species decreased. Aerosol absorption increased faster than scattering during CAP events. Therefore, the mean single scattering albedo decreased from 0.85 during non-CAP periods to 0.81 during CAP events. Radiosonde-sounding observations and numerical simulations indicated that aerosols accumulating in the lower basin heated the atmosphere during the daytime and facilitated boundary layer development via the "stove effect" (absorption aerosol heats lower atmosphere to promote boundary layer development). No significant "dome effect" (absorption aerosol heats the upper boundary layer to suppress boundary layer development) occurred during the two CAP events. These findings provide a theoretical basis for scientifically-guided PM2.5 pollution control in winter in isolated urban basins.

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Cotton is one of the most exploited crops in the world, being one of the most important for the Brazilian Northeast. In this region, the use of irrigation is often necessary to meet the water demand of the crop. Water is often used from underground wells that have a large amount of salt in their constitution, which can compromise the development of crops, so it is vital to adopt strategies that reduce salt stress effects on plants, such as the foliar application of hydrogen peroxide. Thus, the objective of this study was to evaluate the effects of foliar application of hydrogen peroxide on the gas exchange, growth, and production of naturally colored cotton under salt stress in the semi-arid region of Paraíba, Brazil. The experiment was carried out in a randomized block design in a 5 × 5 factorial scheme, with five salinity levels of irrigation water-ECw (0.3, 2.0, 3.7, 5.4 and 7.1 dS m-1)-and five concentrations of hydrogen peroxide-H2O2 (0, 25, 50, 75 and 100 µM), and with three replicates. The naturally colored cotton 'BRS Jade' had its gas exchange, growth, biomass production, and production reduced due to the effects of salt stress, but the plants were able to produce up to the ECw of 3.97 dS m-1. Foliar application of hydrogen peroxide at the estimated concentrations of 56.25 and 37.5 µM reduced the effects of salt stress on the stomatal conductance and CO2 assimilation rate of cotton plants under the estimated ECw levels of 0.73 and 1.58 dS m-1, respectively. In turn, the concentration of 12.5 µM increased water-use efficiency in plants subjected to salinity of 2.43 dS m-1. Absolute and relative growth rates in leaf area increased with foliar application of 100 µM of hydrogen peroxide under ECw of 0.73 and 0.3 dS m-1, respectively. Under conditions of low water salinity (0.3 dS m-1), foliar application of hydrogen peroxide stimulated the biomass formation and production components of cotton.

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Evaluation of land use and land cover (LULC) change is among vital tools used for tracking environmental health and proper resource management. Remote sensing data was used to determine LULC change in Bahi (Manyoni) Catchment (BMC) in central Tanzania. Landsat satellite images from Landsat 5 TM and Landsat 8 OLI/TIRS were used, and support vector machine (SVM) algorithm was applied to classify the features of BMC. The obtained kappa values were 0.74, 0.83 and 0.84 for LULC maps of 1985, 2005 and 2021, respectively, which indicates the degree of accuracy from produced being substantial to almost perfect. Classified maps along with geospatial, socio-economic and climatic drivers with sufficient explanatory power were incorporated into MLP-NN to produce transition potential maps. Transition maps were subsequently used in cellular automata (CA)-Markov chain model to predict future LULC for BMC in immediate-future (2035), mid-future (2055) and far-future (2085). The findings indicate BMC is expected to experience significant expansion of agricultural lands and built land from 31.89 to 50.16% and 1.48 to 9.1% from 2021 to 2085 at the expense of open woodland, shrubland and savanna grassland. Low-yield crop production, water scarcity and population growth were major driving forces for rapid expansion of agricultural lands and overall LULC in BMC. The findings are essential for understanding the impact of LULC on hydrological processes and offer insights for the internal drainage basin (IDB) board to make necessary measures to lessen the expected dramatic changes in LULC in the future while sustaining harmonious balance with livelihood activities.

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The objective of this study was to evaluate the synthesis of photosynthetic pigments, gas exchange, and photochemical efficiency of sour passion fruit genotypes irrigated with saline water under the conditions of the semi-arid region of Paraíba state, Brazil. The experiment was conducted at the experimental farm in São Domingos, PB. A randomized block design was adopted, in a 5 × 3 factorial scheme, with five levels of electrical conductivity of irrigation water-ECw (0.3, 1.1, 1.9, 2.7, and 3.5 dS m-1)-and three genotypes of sour passion fruit (Gigante Amarelo-'BRS GA1'; Sol do Cerrado-'BRS SC1'; and Catarina-'SCS 437'. The increase in the electrical conductivity of irrigation water negatively affected most of the physiological characteristics of the sour passion fruit at 154 days after transplanting. Significant differences were observed between sour passion fruit genotypes when its tolerance was subjected to the salinity of irrigation water. There was an increase in the percentage of damage to the cell membrane with the increase in the electrical conductivity of irrigation water, with maximum values of 70.63, 60.86, and 80.35% for the genotypes 'BRS GA1', 'BRS SC1', and SCS 437', respectively, when irrigated with water of 3.5 dS m-1. The genotype 'BRS Sol do Cerrado' showed an increase in the synthesis of photosynthetic pigments when irrigated with water of 3.5 dS m-1, with maximum values estimated at 1439.23 µg mL-1 (Chl a); 290.96 µg mL-1 (Chl b); 1730.19 µg mL-1 (Chl t); and 365.84 µg mL-1 (carotenoids). An increase in photosynthetic efficiency parameters (F0, Fm, and Fv) of the genotype 'BRS Gigante Amarelo' was observed when cultivated with water with high electrical conductivity (3.5 dS m-1).

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Remote sensing is an important tool for monitoring soil information. However, accurate spatial modeling of soil organic matter (SOM) in areas with high vegetation coverage, typically represented by agroecosystems, remains a challenge for field-scale estimation using remote sensing. To date, studies have focused on using single-period or multi-temporal vegetation information to characterize SOM. Thus, the relationship between SOM content and time-series vegetation biomass has not yet been fully explored. In addition, most studies have ignored the effects of critical soil properties and human activities (e.g., soil salinization, soil particle size fractions, history of land-use changes) on SOM. By integrating information on vegetation, soil, and human activities, we propose a novel framework for assessing SOM in cotton fields of artificial oases in northwest China, where returned straw is one of the primary sources of SOM coming from vegetation. We developed an Annual Maximum Biomass Accumulation Index (AMBAI) using time-series Landsat images from 1990 to 2019. Subsequently, we quantified the information of the planting years (PY) of cropland using spectral index threshold and incorporated proximal sensing data (soil hyperspectral and apparent conductivity data) and soil particle size fractions to establish a predictive model of SOM using partial least squares regression (PLSR), random forest (RF), and convolutional neural network (CNN). The results revealed that AMBAI had the highest correlation coefficient (r) with SOM (0.76, P < 0.01). AMBAI, soil hyperspectral data, and PY were the most relevant predictors for estimating SOM. The CNN model integrating vegetation, soil, and human activity information performed best, with coefficient of determination (R2), relative analysis error (RPD), and root mean square error (RMSE) of 0.83, 2.38 and 1.38 g kg-1, respectively. This study confirmed that AMBAI and PY had great potential for characterizing SOM in arid and semi-arid regions, providing a reference for other relevant studies.

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Understanding the effects of groundwater depth on soil microbiota and multiple soil functions is essential for ecological restoration and the implementation of groundwater conservation. The current impact of increased groundwater levels induced by drought on soil microbiota and multifunctionality remains ambiguous, which impedes our understanding of the sustainability of water-scarce ecosystems that heavily rely on groundwater resources. This study investigated the impacts of altered groundwater depths on soil microbiota and multifunctionality in a semi-arid region. Three groundwater depth levels were studied, with different soil quality and soil moisture at each level. The deep groundwater treatment had negative impacts on diversity, network complexity of microbiota, and the relationships among microbial phylum unites. Increasing groundwater depth also changed composition of soil microbiota, reducing the relative abundance of dominant phyla including Proteobacteria and Ascomycota. Increasing groundwater depth led to changes in microbial community characteristics, which are strongly related to alterations in soil multifunctionality. Overall, our results suggest that groundwater depth had a strongly effect on soil microbiota and functionality.

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Introduction: Understanding the impact of deep groundwater depth on vegetation communities and soil in sand dunes with different underground water tables is essential for ecological restoration and the conservation of groundwater. Furthermore, this understanding is critical for determining the threshold value of groundwater depth that ensures the survival of vegetation. Method: This paper was conducted in a semi-arid region in eastern China, and the effects of deep groundwater depth (6.25 m, 10.61 m, and 15.26 m) on vegetation communities and soil properties (0-200 cm) across three dune types (mobile, semi-fixed, and fixed dunes) were evaluated in a sand ecosystem in the Horqin Sandy Land. Results: For vegetation community, variations in the same species are more significant at different groundwater depths. For soil properties, groundwater depth negatively influences soil moisture, total carbon, total nitrogen, available nitrogen, available phosphorus concentrations, and soil pH. Besides, groundwater depth also significantly affected organic carbon and available potassium concentrations. In addition, herb species were mainly distributed in areas with lower groundwater depth, yet arbor and shrub species were sparsely distributed in places with deeper groundwater depth. Discussion: As arbor and shrub species are key drivers of ecosystem sustainability, the adaptation of these dominant species to increasing groundwater depth may alleviate the negative effects of increasing groundwater depth; however, restrictions on this adaptation were exceeded at deeper groundwater depth.

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Growing food demand challenges the expansion of agriculture, while water and energy shortages have seriously jeopardized agricultural sustainability. Therefore, the water-energy-food (WEF) nexus must be integrated into sustainable agriculture management. However, despite the increasing sophistication of models for WEF optimization, more studies have considered only how to reduce resource consumption and less on how to increase resource supply. This paper outlines an agricultural WEF optimization model based on photovoltaic panel rainwater harvesting (PVRH). The model innovatively incorporates the PVRH system into the agricultural WEF nexus, providing a decision-making framework that exploits and conserves resources in parallel, while contributing to economic benefits. The model was applied in a rural case study in a semi-arid region of China. The results highlight the significant potential of the PVRH system to exploit water and energy, and the increased resources are allocated to irrigated alfalfa and vegetables, which would significantly increase revenue. However, the model does not recommend large-scale vegetable cultivation, which would increase water and energy consumption and reduce the WEF indicator values indicating agricultural sustainability. The final scheme will build a 98.92MWp PV power station, develop 1.31 × 108 kW·h of electricity and 1.97 × 107 m3 of rainwater into agricultural production. And through cropping restructuring, it will increase 23.61 % of economic revenue and save 57.74 % of water and 3.24 % of energy. In general, the model framework is transferable and applicable to similar agricultural areas under semi-arid climatic conditions.

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This study's objective was to evaluate the thermoregulation aspects of native goats through broken-line regression to understand the triggering of physiological responses in the homeothermy process. Data were collected from ten healthy dams of the Canindé breed once a week at hourly intervals (24 h) for eight consecutive weeks. Air temperature (AT; °C), relative humidity (RH; %) were measured and temperature-humidity index (THI) calculated. The thermoregulation parameters evaluated were: respiratory rate (RR; breaths.min-1), rectal temperature (RT; °C) and sweating rate (SR; g.m-2.h-1). All variables were subjected to analysis of variance with repeated measures over time. The hour was considered a fixed effect (00:00 h, 01:00 h, …, 23:00 h), and the animal was a random effect. Multiple regression analyses were also examined using General Linear Models and Variance Inflation Factors were calculated. Broken line non-linear regressions for RR, RT and SR were examined using independent variables. The highest average for AT and RH were 35.9 °C (13:00 h) and 92.4% (04:00 h), respectively. The lowest average of TA and RH were 22.1 °C (05:00 h) and 28.0% (12:00 h), respectively. The highest average THI was 102.1 (13:00 h), and the lowest 78.0 (05:00 h). The environmental thresholds at which RR, RT and SR began to increase for AT were between 17-21 °C and RH were > 17% (RR), ≥ 21 (RT) and > 23% (SR). For THI the limits were 108.4 for RR, 78.0 for RT and 100.1 for SR. Using THI, the thermoregulatory parameters are activated in the following order: SR, RR and RT. Estimates can serve as a basis to implement heat stress mitigation and improve animal welfare strategies for native goats.

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This study explores the adaptation strategies to water shortage at the farm level in the Tunisian semi-arid region and investigates factors determining the farmers' behavior. In this area, climate change exacerbates the scarcity of water resources, groundwater suffers from saline intrusion, and the reinforcement of surface water depends on the water transfer decision. Based on 81 farmers survey data collected in Diar El Hajjaj irrigated area, the typology of farmers was constructed using the multivariate technique combining the clustering method and principal component analysis.The results show that two groups of the surveyed farmers use more intensive water resources to grow high-value crops and operate toward a commercial farming system. They set up either an accommodating or an expansive plan. The remaining sampled farmers are smallholder farmers who endure critical resource constraints. They tend to adopt a contractive strategy and face a real risk of farming abandonment. Additional actions should be taken at the farm level as well at the national policy level to preserve the farming system under water scarcity due to climate change effects.

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Salinization caused by climate change and nitrogen (N) pollution are both important environmental threats for inland lakes. However, evaluating their interactive effects continues to be challenging. Here, field observation and microcosmic experiments were conducted in six lakes of East Asia with the different salinity and climate characteristics, to explore the response of the key N cycle processes related to N fate to the climate-induced change in salinity. The results indicated that increased salinity inhibited denitrification, which was the outcome of two cumulative effects: the long-term microbial adaptation effect and the direct salinity stress. Whereas increased salinity had unsignificant or positive effects on dissimilatory nitrate reduction to ammonium. It had caused that N retention capacity is relatively stronger in saline than freshwater lakes. Inland lakes are long-term basin-wide integrators of climatic conditions that drying (salinization) and wetting (desalination) with climate change. In semi-arid regions of East Asia, lake shrinkage, salinization and increasing temperature driven by climate warming and drying may exert a negative impact on N pollution through concentrating, decreasing denitrification and increasing ammonium release from sediment. The threat of climate change on these lakes is not just the quantity of water, but its quality.

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Soil microbes play crucial roles in grassland ecosystem functions, such as soil carbon (C) pool and nutrient cycle. Soil microbes in grasslands are susceptible to the degradation mediated by climate change and anthropogenic disturbance. However, research on how the degradation influences the diversity and community structure of different soil microbial taxa is relatively scarce. We conducted a large-scale field survey to describe the effects of four degradation levels (PD: potential degradation, LD: light degradation, MD: moderate degradation, and SD: severe degradation) on soil bacterial and fungal community in the semi-arid grasslands of northern China. We found that soil moisture, nutrients, and clay content decreased, but soil sand content increased along the increasing degradation gradient. However, the degradation had no effects on soil pH and the C:N ratio. Grassland degradation had non-significant effect on soil bacterial diversity, but it significantly affected soil bacterial community structure. The degradation decreased soil fungal diversity and had a relatively larger influence on the community structure of soil fungi than that of bacteria. The community composition and structure of soil fungi were mainly affected by soil nutrients and texture, while those of soil bacteria were mainly affected by soil pH. These results indicate that changes in soil properties induced by grassland degradation mainly drive the variation in the soil fungal community and have less effect on the soil bacterial community. This study reveals the sensitivity of soil fungal community to grassland degradation, highlighting the priority of soil fungal community for the management and restoration of degraded grasslands.

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To understand the enrichment factors and pollution levels of heavy metals in agricultural soils in the semi-arid region of Hainan island, 1818 surface soil samples were collected in Gancheng Town and analyzed for their heavy metal contents and physicochemical composition. Correlation analysis was used to determine the heavy metal enrichment factors. The geo-accumulation index (Igeo), comprehensive ecological risk index (RI), and hazard index (HI), as well as carcinogenic risk (CR), were used to assess the degree of pollution and health risk. Positive matrix factorization (PMF) was used to determine the primary sources of pollution and priority sources. The average values of heavy metal contents in the topsoil were 22.7, 0.128, 33.4, 14.5, 0.032, 9.32, 32.5, and 43.3 mg·kg-1 for As, Cd, Cr, Cu, Hg, Ni, Pb, and Zn, respectively. With the exception of Zn, the concentrations of other heavy metals in the topsoil were higher than the soil background values of Hainan, showing different degrees of heavy metal accumulation effect. The Igeo revealed that the major pollutant element in soils was As, followed by Cd and Cu. The RI showed that the proportion of soil samples that were high-risk level or worse was 29.4% of the total number of samples, among which As was the major source of risk. The health risk assessment results indicated that As, Cr, and Ni exposure presented carcinogenic risk for children with high CR values. Based on PMF, four major sources of heavy metals were identified in the study area. Hg was derived mainly from industrial sources, and As was closely associated with agricultural activities. Ni, Cu, Cr, and Zn were related to soil parent materials. Pb and Cd were associated with agricultural activities and traffic emissions. The PMF models combined with correlation analysis were useful for estimating the source apportionment of heavy metals in soils.

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Since the (Covid-19) pandemic outbreak, questioning regarding climate and incident of Covid-19 infection rates has been debated, while there is no clear research evidence until now in Iran. This study has focused on investigating the association between Covid-19 cases and demographic -meteorological factors in arid and semi-arid zones of Iran (from March 1, 2020, to January 31, 2022) by analyzing with Via Poisson and negative binomial regression. As a result, the incidence rate of both Covid-19 hospitalization and mortality cases reached peaks in the summer followed by the autumn. Interestingly, Covid-19 hospitalization cases are associated with humidity, temperature, and wind factors seasonally and monthly, but mortality cases are just associated with wind. In conclusion, the result demonstrated that demographicand meteorological factorsare positively and negatively associated with Covid-19 cases. Therefore, identifying the environmental factors contributing to the excess Covid-19 can help to prevent future pandemic waves in Iranian arid and semi-arid zone.