Significant spatiotemporal pattern of nitrous oxide emission and its influencing factors from a shallow eutropic lake in Inner Mongolia, China.

Eutrophic shallow lakes are generally considered as a contributor to the emission of nitrous oxide (N2O), while regional and global estimates have remained imprecise. This due to a lack of data and insufficient understanding of the multiple contributing factors. This study characterized the spatiotemporal variability in N2O concentrations and N2O diffusive fluxes and the contributing factors in Lake Wuliangsuhai, a typical shallow eutrophic and seasonally frozen lake in Inner Mongolia with cold and arid climate. Dissolved N2O concentrations of the lake exhibited a range of 4.5 to 101.2 nmol/L, displaying significant spatiotemporal variations. The lowest and highest concentrations were measured in summer and winter, respectively. The spatial distribution of N2O flux was consistent with that of N2O concentrations. Additionally, the hotspots of N2O emissions were detected within close to the main inflow of lake. The wide spatial and temporal variation in N2O emissions indicate the complexity and its relative importance of factors influencing emissions. N2O emissions in different lake zones and seasons were regulated by diverse factors. Factors influencing the spatial and temporal distribution of N2O concentrations and fluxes were identified as WT, WD, DO, Chl-a, SD and COD. Interestingly, the same factor demonstrated opposing effects on N2O emission in various seasons or zones. This research improves our understanding of N2O emissions in shallow eutrophic lakes in cold and arid areas.

[Evolution Trend of Water Quality in the Inner Mongolia Section of the Yellow River from 2003 to 2020].

The aim of this study was to comprehensively understand the water environment quality status and its change trend in the Inner Mongolia section of the Yellow River Basin. To analyze the water quality in recent years,the water quality data in the Yellow River basin from 2003 to 2020 were firstly collected from five typical monitoring stations．Various data analysis methods, including principal component analysis, cluster analysis, and a long short-term memory model, were used along with an improved comprehensive water quality identification index to explore the spatiotemporal characteristics of water quality in the Yellow River Basin. The results showed that the overall water quality in the basin has improved and stabilized over time. In terms of temporal variation, there was a distinction between the wet season and dry season, with a better status observed during the wet season due to increased agricultural irrigation and higher water volume. Spatially, the five monitoring sections could be divided into three categories based on strong natural factors that maintained their temporal characteristics during the wet season； however, significant differences were observed during the dry season due to urban water usage patterns. Analysis using LSTM models revealed that ammonia nitrogen will continue to decline and have a decreasing impact on the comprehensive water quality. These findings provide valuable insights for the comprehensive management of water quality in Inner Mongolia's Yellow River Basin.

Dynamics of sediment phosphorus in the middle and lower stretch of River Ganga, India: insight into concentration, fractionation, and environmental risk assessment of phosphorus.

Despite continuous efforts, eutrophication is still occurring in freshwater and phosphorus (P) is the most important nutrients that drive the eutrophication in rivers and streams. However, little information is available about the distribution of P fractions in river sediment. Here, the sequential extraction approach was used to evaluate the sediment P fractionation and its content in the anthropogenically damaged river Ganga, India. Different sedimentary P fractions viz. exchangeable (Ex-P), aluminum bound (Al-P), iron bound (Fe-P), calcium bound (Ca-P), and organically bound phosphorus (Org-P), were quantified. Significantly higher level of total P was recorded in pre-monsoon season (438.5 ± 95.8 mg/kg), than other [winter (345.7 ± 110.6 mg/kg), post-monsoon (319.2 ± 136.3 mg/kg), and monsoon (288.6 ± 77.3 mg/kg)] seasons. Different P fractions such as Ex-P, Al-P, Fe-P, Ca-P and Org-P varied from 2.88-12.8 mg/kg, 7.64-98.8 mg/kg, 32.2-179.2 mg/kg, 51.97-286.1 mg/kg and 9.3-143.7 mg/kg, respectively, which correspondingly represented 0.5-10.54%, 3.41-20.18%, 17.27-37.82%, 37.35-60.2%, 4.15-25.88% of the Total P with a rank order of P-fractions was Ca-P > Fe-P > Org-P > Al-P > Ex-P. Bio-available P contributes a considerable portion (37.9-46.0%) of total P which may increase the eutrophication to overlying water. Results demonstrate that inorganic P species control the P bio-availability in both time and space. However, an estimated phosphorus pollution index based on sediment total P content showed no ecological risk of phosphorus to Ganga River sediment.

[Multi-factor Impact Analysis of Grassland Phenology Changes on the Qinghai-Xizang Plateau Based on Interpretable Machine Learning].

The vegetation phenology of the Qinghai-Xizang Plateau is changing significantly in the context of climate change. However, there are many hydrothermal factors affecting the phenology, and few studies have focused on the effects of multiple factors on the phenology of the Qinghai-Xizang Plateau, resulting in a lack of understanding of the mechanisms underlying phenological changes on the Qinghai-Xizang Plateau. In this study, we used remote sensing data interpretation to analyze the spatial and temporal variability of grassland phenology on the Qinghai-Xizang Plateau from 2002 to 2021, focusing on precipitation, temperature, altitude, soil, and other aspects to reveal the dominant factors of phenological variability using an interpretable machine learning method (SHAP) and to quantify the interactive effects of multiple factors on phenology. The results showed that:① The growing season start (SOS) of grasslands on the Qinghai-Xizang Plateau mostly ranged from 110 to 150 d, with 56.32 % of grasslands showing an early SOS trend; the growing season end (EOS) mostly ranged from 290-320 d, with 67.65 % of grasslands showing a delayed EOS trend; and the growing season length (LOS) mostly ranged from 120 to 210 d, with 65.50 % of the grasslands showing a trend towards longer growing season lengths. ② SOS in grasslands on the Qinghai-Xizang Plateau was mainly influenced by moisture conditions, in which soil moisture between 10 and 25 kg·m-2 in the 0-10 cm soil layer in March promoted the advancement of SOS and peaked at approximately 20 kg·m-2. EOS was mainly influenced by temperature, with higher temperatures in September and October having a stronger effect on EOS latency promotion and peaking at over 8 ℃ and -0.5 ℃, respectively. The main influencing factors of LOS were more consistent with SOS, in which soil moisture between 15 and 25 kg·m-2 in the 0-10 cm soil layer in March promoted the prolongation of LOS and peaked at approximately 18 kg·m-2. ③ There was an obvious interactive effect of water and heat and other factors on phenology; after soil moisture reached 20 kg·m-2 in the 0-10 cm soil layer in March, SOS was more advanced in low-precipitation and low-altitude areas. Better moisture conditions were more conducive to EOS delay at temperatures above 0 ℃ in October, and soil moisture in high precipitation areas promoted LOS prolongation more when soil moisture was between 12 and 22 kg·m-2 in 0-10 cm in March. The results also demonstrated that interpretable machine learning methods could provide a new approach to the analysis of the multifactorial effects of phenological change.

Restoration of vegetation in the Yellow River Basin of Inner Mongolia is limited by geographic factors.

Studying the relationships between vegetation cover and geography in the Mongolian region of the Yellow River Basin will help to optimize local vegetation recovery strategies and achieve harmonious human relations. Based on MOD13Q1 data, the spatial and temporal variations in fractional vegetation cover (FVC) in the Mongolian Yellow River Basin during 2000-2020 were investigated via trend and correlative analysis. The results are as follows: (1) From 2000 to 2020, the vegetation cover in the Mongolian section of the Yellow River Basin recovered well, the mean increase in the FVC was 0.001/a, the distribution of vegetation showed high coverage in the southeast and low coverage in the northwest, and 31.19% of the total area showed an extremely significant and significant increase in vegetation cover. (2) The explanatory power of each geographic factor significantly differed. Precipitation, soil type, air temperature, land use type and slope were the main driving factors influencing the spatial distribution of the vegetation cover, and for each factor, the explanatory power of its interaction with other factors was greater than that of the single factor. (3) The correlation coefficients between FVC and temperature and precipitation are mainly positive. The mean value of the FVC and its variation trend are characterized by differences in terrain and soil characteristics, population density and land use. Land use conversion can reflect the characteristics of human activities, and positive effects, such as returning farmland to forest and grassland and afforestation of unused land, promote the significant improvement of regional vegetation, while negative effects, such as urban expansion, inhibit the growth of vegetation.

U-Net Convolutional Neural Network for Mapping Natural Vegetation and Forest Types from Landsat Imagery in Southeastern Australia.

Accurate and comparable annual mapping is critical to understanding changing vegetation distribution and informing land use planning and management. A U-Net convolutional neural network (CNN) model was used to map natural vegetation and forest types based on annual Landsat geomedian reflectance composite images for a 500 km × 500 km study area in southeastern Australia. The CNN was developed using 2018 imagery. Label data were a ten-class natural vegetation and forest classification (i.e., Acacia, Callitris, Casuarina, Eucalyptus, Grassland, Mangrove, Melaleuca, Plantation, Rainforest and Non-Forest) derived by combining current best-available regional-scale maps of Australian forest types, natural vegetation and land use. The best CNN generated using six Landsat geomedian bands as input produced better results than a pixel-based random forest algorithm, with higher overall accuracy (OA) and weighted mean F1 score for all vegetation classes (93 vs. 87% in both cases) and a higher Kappa score (86 vs. 74%). The trained CNN was used to generate annual vegetation maps for 2000-2019 and evaluated for an independent test area of 100 km × 100 km using statistics describing accuracy regarding the label data and temporal stability. Seventy-six percent of pixels did not change over the 20 years (2000-2019), and year-on-year results were highly correlated (94-97% OA). The accuracy of the CNN model was further verified for the study area using 3456 independent vegetation survey plots where the species of interest had ≥ 50% crown cover. The CNN showed an 81% OA compared with the plot data. The model accuracy was also higher than the label data (76%), which suggests that imperfect training data may not be a major obstacle to CNN-based mapping. Applying the CNN to other regions would help to test the spatial transferability of these techniques and whether they can support the automated production of accurate and comparable annual maps of natural vegetation and forest types required for national reporting.

Neonicotinoid insecticides in well-developed agricultural cultivation areas: Seawater occurrence, spatial-seasonal variability and ecological risks.

Neonicotinoids (NEOs) are widely used insecticides and have been detected in aquatic environments globally. However, little is known about NEOs contamination in the coastal environments under the terrestrial pressure of multiple planting types simultaneously. This study investigated the occurrence, spatial-seasonal variability, and ecological risks of NEOs along the coast of the Shandong Peninsula during the dry and wet seasons, where located many largest fruit, vegetable, and grain production bases in China. The concentrations of ∑NEOs in seawater were higher in wet seasons (surface: 195.46 ng/L; bottom: 14.56 ng/L) than in dry seasons (surface: 10.07 ng/L; bottom: 8.45 ng/L). During the wet seasons, NEOs peaked in the northern and eastern areas of the Shandong Peninsula, where the inland fruit planting area is located. While dry seasons had higher concentrations in Laizhou Bay, influenced by rivers from vegetable-growing areas. Grain crops, fruit, and cotton planting were major NEOs sources during wet seasons, while wheat and vegetables dominated in dry seasons. Moderate or above ecological risks appeared at 53.8% of the monitoring sites. Generally, NEOs caused high risks in the wet seasons mainly caused by Imidacloprid, and medium risk in the dry seasons caused by Clothianidin, which should be prevented and controlled in advance.

Atmospheric occurrences and bioavailability health risk of PAHs and their derivatives surrounding a non-ferrous metal smelting plant.

Non-ferrous metal smelting emits large amounts of organic compounds into the atmosphere. Herein, 20 parent polycyclic aromatic hydrocarbons (PPAHs), 9 nitrated PAHs (NPAHs), 14 chlorinated PAHs (ClPAHs), and 6 alkylated PAHs (APAHs) in atmospheric samples from a typical non-ferrous metal smelting plant (NMSP) and residential areas were detected. In NMSP, benzo[a]pyrene, dibenz[a,h]anthracene, 6-nitrochrysene, 9-chlorofluorene, and 1-methylfluorene were the predominant compounds in the particulate phase, while phenanthrene constituted 57.3% in the gaseous phase. The concentration of PAHs in residential areas around NMSP was 1.8 times higher than that in the control area. Additionally, there was a significant negative correlation between the concentration and the distance from the NMSP. In terms of health risks, although the skin penetration coefficient of PM2.5 is smaller than that of the gaseous phase, dermal absorption of PM2.5 posed a greater threat to the population, the incremental lifetime cancer risk (ILCR) of NMSP was 1.8 × 10-4. After considering bioavailability, BILCR decreased by 1-2 orders of magnitude in different regions, and dermal absorption decreased more than inhalation intake. Nevertheless, the dermal absorption of PM2.5 in NMSP still presents a probable carcinogenic risk. This study provides a necessary reference for the subsequent control of NMSP contamination.

A comprehensive drought index based on spatial principal component analysis and its application in northern China.

In the background of the greenhouse effect, drought events occurred more frequently. How to monitor drought events scientifically and efficiently is very urgent at present. In this study, we employed the Vegetation Water Supply Index (VSWI), Temperature Vegetation Drought Index (TVDI), and Crop Water Stress Index (CWSI) as individual variables to construct a composite drought index (CDI) using spatial principal component analysis (SPCA). The validity of CDI was assessed using gross primary productivity (GPP), soil moisture (SM), Standardized Precipitation Evapotranspiration Index (SPEI), and Vegetation Condition Index (VCI). CDI was subsequently used for drought monitoring in northern China from 2011 to 2020. The results showed that (1) at a 99% confidence level, the Pearson correlation coefficients between CDI and GPP was 0.72, while the value between CDI and SM was 0.69, which indicated the relationship between SM, GPP, and CDI was significant. (2) We compared CDI with other variables such as Standardized Precipitation Evapotranspiration Index (SPEI) and Crop Drought Index (CDI) and found that the monitoring result of CDI was more sensitive, which indicated that the proposed CDI had a better effect in local drought monitoring. (3) The results of CDI showed that the drought status in the northern region during 2011-2020 lasted from March to October, and the high severe drought period generally occurs in March-May and September-October, with low severe drought in June-August.

Spatio-temporal variation characteristics of stable isotopes of tap water and its potential as a proxy for surface water in Sichuan, China.

The stability and safety of tap water are essential for human health and economic and social development. The stable isotopes can be used to reveal characteristics of tap water and link it with its source. In this paper, 1556 tap water samples were collected from Sichuan, China and the stable isotope ratios for these samples were determined. The δ2H ranges from -126.4 ‰ to -26.4 ‰, and the range of δ18O is -17.04 ‰ to -2.08 ‰, reflecting the tap water sources are affected by complex spatial features and changing meteorological elements. Stable isotopes in tap water usually reach the maximum values in summer, indicating that heavy isotope enrichment is easily achievable by the large amount of evaporation from water sources during the summer season. By using spatial interpolation and isoscapes, we can find that there is a strong correlation between both simulated tap water δ2H and river water δ2H, with the maximum difference not exceeding 10.0 ‰, while the overall mean relative error is 6 %. Consequently, it is feasible to use tap water isotopes as a proxy for surface water isotopes in representative watersheds where surface water is the main source of water. The study shows the variation characteristics and influencing factors of tap water isotopes and enriches the isotope database of tap water in China. Meanwhile, the utilize of stable isotopes in tap water as a proxy for surface water expands the application field of tap water stable isotopes and opens new perspectives for indirectly obtaining isotope data of surface water.

Associations between medium- and long-term exposure to air temperature and epigenetic age acceleration.

Climate change poses a serious threat to human health worldwide, while aging populations increase. However, no study has ever investigated the effects of air temperature on epigenetic age acceleration. This study involved 1,725 and 1,877 participants from the population-based KORA F4 (2006-2008) and follow-up FF4 (2013-2014) studies, respectively, conducted in Augsburg, Germany. The difference between epigenetic age and chronological age was referred to as epigenetic age acceleration and reflected by Horvath's epigenetic age acceleration (HorvathAA), Hannum's epigenetic age acceleration (HannumAA), PhenoAge acceleration (PhenoAA), GrimAge acceleration (GrimAA), and Epigenetic Skin and Blood Age acceleration (SkinBloodAA). Daily air temperature was estimated using hybrid spatiotemporal regression-based models. To explore the medium- and long-term effects of air temperature modeled in time and space on epigenetic age acceleration, we applied generalized estimating equations (GEE) with distributed lag non-linear models, and GEE, respectively. We found that high temperature exposure based on the 8-week moving average air temperature (97.5th percentile of temperature compared to median temperature) was associated with increased HorvathAA, HannumAA, GrimAA, and SkinBloodAA: 1.83 (95% CI: 0.29-3.37), 11.71 (95% CI: 8.91-14.50), 2.26 (95% CI: 1.03-3.50), and 5.02 (95% CI: 3.42-6.63) years, respectively. Additionally, we found consistent results with high temperature exposure based on the 4-week moving average air temperature was associated with increased HannumAA, GrimAA, and SkinBloodAA: 9.18 (95% CI: 6.60-11.76), 1.78 (95% CI: 0.66-2.90), and 4.07 (95% CI: 2.56-5.57) years, respectively. For the spatial variation in annual average temperature, a 1 °C increase was associated with an increase in all five measures of epigenetic age acceleration (HorvathAA: 0.41 [95% CI: 0.24-0.57], HannumAA: 2.24 [95% CI: 1.95-2.53], PhenoAA: 0.32 [95% CI: 0.05-0.60], GrimAA: 0.24 [95%: 0.11-0.37], and SkinBloodAA: 1.17 [95% CI: 1.00-1.35] years). In conclusion, our results provide first evidence that medium- and long-term exposures to high air temperature affect increases in epigenetic age acceleration.

The composition of environmental microbiota in three tree fruit packing facilities changed over seasons and contained taxa indicative of L. monocytogenes contamination.

BACKGROUND: Listeria monocytogenes can survive in cold and wet environments, such as tree fruit packing facilities and it has been implicated in outbreaks and recalls of tree fruit products. However, little is known about microbiota that co-occurs with L. monocytogenes and its stability over seasons in tree fruit packing environments. In this 2-year longitudinal study, we aimed to characterize spatial and seasonal changes in microbiota composition and identify taxa indicative of L. monocytogenes contamination in wet processing areas of three tree fruit packing facilities (F1, F2, F3). METHODS: A total of 189 samples were collected during two apple packing seasons from floors under the washing, drying, and waxing areas. The presence of L. monocytogenes was determined using a standard culturing method, and environmental microbiota was characterized using amplicon sequencing. PERMANOVA was used to compare microbiota composition among facilities over two seasons, and abundance-occupancy analysis was used to identify shared and temporal core microbiota. Differential abundance analysis and random forest were applied to detect taxa indicative of L. monocytogenes contamination. Lastly, three L. monocytogenes-positive samples were sequenced using shotgun metagenomics with Nanopore MinION, as a proof-of-concept for direct detection of L. monocytogenes' DNA in environmental samples. RESULTS: The occurrence of L. monocytogenes significantly increased from 28% in year 1 to 46% in year 2 in F1, and from 41% in year 1 to 92% in year 2 in F3, while all samples collected from F2 were L. monocytogenes-positive in both years. Samples collected from three facilities had a significantly different microbiota composition in both years, but the composition of each facility changed over years. A subset of bacterial taxa including Pseudomonas, Stenotrophomonas, and Microbacterium, and fungal taxa, including Yarrowia, Kurtzmaniella, Cystobasidium, Paraphoma, and Cutaneotrichosporon, were identified as potential indicators of L. monocytogenes within the monitored environments. Lastly, the DNA of L. monocytogenes was detected through direct Nanopore sequencing of metagenomic DNA extracted from environmental samples. CONCLUSIONS: This study demonstrated that a cross-sectional sampling strategy may not accurately reflect the representative microbiota of food processing facilities. Our findings also suggest that specific microorganisms are indicative of L. monocytogenes, warranting further investigation of their role in the survival and persistence of L. monocytogenes. Video Abstract.

Spatial and temporal variations of soil nutrients of cropland in Northeast China from the 1980s to the 2010s.

In recent years, the degradation of cropland soils in Northeast China, such as "thinned, barren, and hardened", has become increasingly serious, with consequences on sustainable development of agriculture. Based on the data from Soil Types of China (1980s) and Soil Series of China (2010s), we examined the change patterns of soil nutrient conditions across different regions and soil types in Northeast China over the past 30 years through the statistical analysis of large samples. The results showed that: 1) from the 1980s to the 2010s, soil nutrient indicators in Northeast China changed to different degrees. Soil pH decreased by 0.03. Soil organic matter (SOM) content decreasd most prominently, by 8.99 g·kg-1 or 23.6%. Soil total N (TN), total P (TP) and total K (TK) contents showed increasing trends, with increases of 17.1%, 46.8%, and 4.9%, respectively. 2) Changes in soil nutrient indicators differed across different provinces and cities. Soil acidification in Liaoning was the most obvious, with pH decreasing by 0.32. SOM content in Liaoning decreased most significantly, by 31.0%. Soil TN, TP, and TK contents in Liaoning increased most significantly by 73.8%, 248.1% and 44.0%, respectively. 3) The changes of soil nutrients varied greatly among soil types, with brown soil and kastanozems showing the greatest decrease in pH. The SOM content of all soil types showed a decreasing trend, with 35.4%, 33.8% and 26.0% reduction in brown soil, dark brown forest soil and chernozem respectively. The greatest increase in TN, TP and TK contents were observed in brown soil by 89.1%, 232.8%, and 48.5%, respectively. In summary, declining organic matter content and soil acidification were the core problems of soil degradation in Northeast China from the 1980s to the 2010s. Reasonable tillage methods and targeted conservation strategies are critically needed to ensure the sustai-nable development of agriculture in Northeast China.

Fipronil and its transformation products in the Yangtze River: Assessment for ecological risk and human exposure.

Fipronil (FP), a phenylpyrazole insecticide, is widely used in agricultural, residential, and veterinary settings. It is toxic to ecosystems and humans; moreover, some of its transformation products are more toxic than FP. A comprehensive profile of the contamination of the Yangtze River by FP and its transformation products (FPs) is not yet available. This study aims to fill this data gap. A total of 144 water samples were collected from 72 sampling locations along the river during the wet (June 2021) and dry (December 2020) seasons. High detection rates (85.4-91.7%) of FPs were found, with ΣFPs' median concentration of 0.49 ng/L. The parent compound FP was the most abundant (median: 0.13 ng/L), followed by FP-desulfinyl (0.08), FP-sulfone (0.07), FP-detrifluoromethylsulfinyl (DTF, 0.07), FP-sulfide (0.06) and FP-amide (0.06). Their concentrations increased significantly from the upper to the lower reaches; for approximately every 100 km toward the lower reaches, the level of FPs increased by 13-15%. The urban region and wet season had the higher FPs contamination. Through water ingestion, the human exposure risk posed by FPs in the river was acceptable; however, the ecological risk assessment showed a moderate to high risk posed by FPs. Follow-up studies are warranted to establish integrated ecological risk assessment models and conduct epidemiological risk assessments among population groups with high exposure levels of FPs. Given the high ecological risk of FPs, regular monitoring of them in the Yangtze River is necessary. FP-DTF was reported in surface water for the first time.

The Impact of Urban Construction Land Change on Carbon Emissions-A Case Study of Wuhan City.

Urban construction land (UCL) change is a significant cause of changes in urban carbon emissions. However, as the extent of this effect is currently unclear, cities cannot easily formulate reasonable carbon reduction policies in terms of land use. Taking the city of Wuhan, China, as an example, this paper combines data on land use and carbon emissions from 1995 to 2019 and uses spatial analysis, curve estimation, and correlation evaluation to explore the direct and indirect effects of the UCL changes on carbon emissions. The results show that: (1) Between 1995 and 2019, the UCL area in Wuhan increased by 193.44%, and carbon emissions increased by 78.63%; moreover, both changes showed a gradually increasing spatial correlation, and the quantitative relationship could be better fitted with a composite function model; (2) The UCL change had mainly an indirect impact on carbon emissions via factors such as population and energy use intensity per unit of carbon emissions; (3) The maximum value of carbon emissions inside a unit area decreased during the study period, with an average annual decrease of about 2.02%. Therefore, the city of Wuhan can promote the achievement of its carbon emissions reduction targets by improving the existing land use policies, for example, by dividing the city into multiple functional zones.

Does Climate Change Increase Crop Water Requirements of Winter Wheat and Summer Maize in the Lower Reaches of the Yellow River Basin?

With increasing water resources stress under climate change, it is of great importance to deeply understand the spatio-temporal variation of crop water requirements and their response to climate change for achieving better water resources management and grain production. However, the quantitative evaluation of climate change impacts on crop water requirements and the identification of determining factors should be further explored to reveal the influencing mechanism and actual effects thoroughly. In this study, the water requirements of winter wheat and summer maize from 1981 to 2019 in the lower reaches of the Yellow River Basin were estimated based on the Penman-Monteith model and crop coefficient method using daily meteorological data. Combined with trends test, sensitivity and contribution analysis, the impacts of different meteorological factors on crop water requirement variation were explored, and the dominant factors were then identified. The results indicated that the temperature increased significantly (a significance level of 0.05 was considered), whereas the sunshine duration, relative humidity and wind speed decreased significantly from 1981 to 2019 in the study area. The total water requirements of winter wheat and summer maize presented a significant decreasing trend (-1.36 mm/a) from 1981 to 2019 with a multi-year average value of 936.7 mm. The crop water requirements of winter wheat was higher than that of summer maize, with multi-year average values of 546.6 mm and 390.1 mm, respectively. In terms of spatial distribution patterns, the crop water requirement in the north was generally higher than that in the south. The water requirements of winter wheat and summer maize were most sensitive to wind speed, and were less sensitive to the minimum temperature and relative humidity. Wind speed was the leading factor of crop water requirement variation with the highest contribution rate of 116.26% among the considered meteorological factors. The results of this study will provide important support for strengthening the capacity to cope with climate change and realizing sustainable utilization of agricultural water resources in the lower reaches of the Yellow River Basin.

Distribution of flame retardants among indoor dust, airborne particles and vapour phase from Beijing: spatial-temporal variation and human exposure characteristics.

The occurrence and distribution of 10 brominated flame retardants (BFRs) and 10 organophosphate flame retardants (OPFRs) were investigated in indoor dust, total suspended particles (TSP), and vapour phase from offices (n = 10), homes (n = 9), and day-care centres (n = 10) in Beijing, China. Three types of samples were collected biweekly from one office and one home over a year to examine temporal trends. BFRs in dust significantly correlated with those in TSP, while OPFRs significantly correlated among all three matrices. In addition, BFRs in dust (ng/g) and TSP (pg/m3) exhibited similar temporal trends with higher levels in the cold season, whereas OPFRs in TSP and vapour phase (pg/m3) showed similar temporal trends with higher levels in the warm season. The geometric mean concentrations of BFRs and OPFRs in the three matrices from the above mentioned three types of indoor microenvironments were used for exposure and health risk estimation, and ∑7OPFRs showed much higher hazard index (HI) values than ∑10BFRs for all subpopulations, and inhalation of OPFRs was a major risk source. With the volatility of flame retardants (FRs) decreasing, the contribution of dust ingestion and dermal absorption showed an increasing trend, and the contribution of inhalation exhibited a gradual decreasing trend, which implied the dominant exposure pathway to FRs is strongly related to the vapour pressure (25 °C, Pa) of these substances. Using a single type of microenvironment or the collection of samples at a single point in time can lead to overestimation or underestimation of overall exposure and risk for people to some extent. The correlations of FRs in dust, TSP, and vapour phase from indoor microenvironments, as well as their temporal trends were first reported in this study, which will provide a basis for more accurate FR exposure assessments in the future.

Relationship between Vegetation Habitats and Bird Communities in Urban Mountain Parks.

The coastal areas of southeast China have typical high-density urbanization characteristics, and urban mountain parks are important green spaces in these urban green space systems. Our study was conducted in five typical urban mountain parks in Fuzhou, China. The bird survey was carried out in 25 transects of different vegetation habitats for 10 months, and the vegetation survey was conducted in 25 habitats to investigate the characteristics of bird communities in different vegetation habitats and the causes of their differences. The results showed: (1) From 1 October 2021 to 15 July 2022, we recorded a total of 90 bird species in 8 orders, 37 families, and 64 genera, with 1879 individuals in five vegetation habitats in the urban mountain parks. (2) Abundance and diversity of trees are vegetation variables affecting bird diversity (bird species richness, abundance, and Shannon diversity) in urban mountain parks, and the average branch height under trees has a significant effect on bird evenness. (3) We found more bird species and higher bird diversity in both sparse and dense forest habitats, but fewer bird species in waterfront, shrub, and grassland habitats; (4) Average tree height (AVE_HEIt) was only present in the best model of bird abundance and evenness. However, none of the best models found a significant effect of the number of tourists and predators on bird diversity. Our results could provide a theoretical basis and guidance for the future improvement of ecological service functions of bird habitats in urban mountain parks in subtropical coastal areas.

Assessing and Validating the Ability of Machine Learning to Handle Unrefined Particle Air Pollution Mobile Monitoring Data Randomly, Spatially, and Spatiotemporally.

Many epidemiological studies have evaluated the accuracy of machine learning models in predicting levels of particulate number (PN) and black carbon (BC) pollutant concentrations. However, few studies have investigated the ability of machine learning to predict the pollutant concentration with using unrefined mobile measurement data and explore the reliability of the prediction models. Additionally, researchers are moving away from using fixed-site data in favor of using mobile monitoring data in a variety of locations to develop hourly empirical models of particulate air pollution. This study compared the differences between long-term (daily average) and short-term (hourly average and 1 s unrefined data) model performance in three different classes of cross validation: randomly, spatially, and spatially temporally. This study used secondary data describing BC and PN pollutant levels in the rural location of Blacksburg (VA). Our results show that the model based on unrefined data was able to detect the pollutant hot spot areas with similar accuracy compared to the aggregated model. Moreover, the performance was found to improve when temporal data added to the model: the 10-fold MAE for the BC and PN were 0.44 µg/m3 and 3391 pt/cm3, respectively, for the unrefined data (one second data) model. The findings detailed here will add to the literature on the correlation between data (pre)processing and the efficacy of machine learning models in predicting pollution levels while also enhancing our understanding of more reliable validation strategies.

Spatial and temporal distribution of arsenic contamination in groundwater of Nawalparasi-West, Nepal: an investigation with suggested countermeasures for South Asian Region.

Nawalparasi-West/Parasi is one of the severely affected districts in the Terai lowlands of Nepal by arsenic (As) contamination in groundwater, exceeding standards of 10 ppb (WHO) and 50 ppb (Nepal Drinking Water Standard). This study presents the spatial and temporal distribution of As across 6 km × 10 km region in Parasi via meteorological, hydrogeological, physio-chemical, and sedimentological investigations in 31 communities for about 5 years. In this study, water balance analysis was carried out for understanding the groundwater dynamics in the study area and its contribution to As elution. Gentle flow gradient and little to no infiltration was observed in the central region with relatively impervious silty clayey flood plain, where higher As concentrations were obtained compared to the northern Siwalik foothills and southern parts with coarser sediments. Similarly, higher As concentration (1048 ppb) was recorded in the drier pre-monsoon season than the wet season (529 ppb). The aquifer at 12 to 23 m depth feeding 73% wells in the study area exhibited higher As concentration in reduced environment as opposed to the oxidizing state at 5- to 6-m and 30- to 50-m deep aquifers. Other constituents such as Fe, B, and Cr and their relation with As were analyzed. The results of GERAS model analysis done for health risk assessment are also presented which show that under long-term exposure, the residents in Parasi were undertaking intolerable cancer risk of 1.1 to 6.4 × 10-3. This study further incorporates socio-economic sentiments vital to analyze, and propose sustainable and cheap countermeasures for immediate implementation to reduce As exposure and health risk in Nepal, which is also highly applicable for other affected regions in South Asian Region.