Exploring the Environmental Influences and Community Assembly Processes of Bacterioplankton in a Subtropical Coastal System: Insights from the Beibu Gulf in China.

Due to rapid urbanization, the Beibu Gulf, a semi-closed gulf in the northwestern South China Sea, faces escalating ecological and environmental threats. Understanding the assembly mechanisms and driving factors of bacterioplankton in the Beibu Gulf is crucial for preserving its ecological functions and services. In the present study, we investigated the spatiotemporal dynamics of bacterioplankton communities and their assembly mechanisms in the Beibu Gulf based on the high-throughput sequencing of the bacterial 16S rRNA gene. Results showed significantly higher bacterioplankton diversity during the wet season compared to the dry season. Additionally, distinct seasonal variations in bacterioplankton composition were observed, characterized by an increase in Cyanobacteria and Thermoplasmatota and a decrease in Proteobacteria and Bacteroidota during the wet season. Null model analysis revealed that stochastic processes governed bacterioplankton community assembly in the Beibu Gulf, with drift and homogenizing dispersal dominating during the dry and wet seasons, respectively. Enhanced deterministic assembly of bacterioplankton was also observed during the wet season. Redundancy and random forest model analyses identified the physical properties (e.g., salinity and temperature) and nutrient content (e.g., nitrate) of water as primary environmental drivers influencing bacterioplankton dynamics. Moreover, variation partitioning and distance-decay of similarity revealed that environmental filtering played a significant role in shaping bacterioplankton variations in this rapidly developed coastal ecosystem. These findings advance our understanding of bacterioplankton assembly in coastal ecosystems and establish a theoretical basis for effective ecological health management amidst ongoing global changes.

The genome-wide signature of short-term temporal selection.

Despite evolutionary biology's obsession with natural selection, few studies have evaluated multigenerational series of patterns of selection on a genome-wide scale in natural populations. Here, we report on a 10-y population-genomic survey of the microcrustacean Daphnia pulex. The genome sequences of [Formula: see text]800 isolates provide insights into patterns of selection that cannot be obtained from long-term molecular-evolution studies, including the following: the pervasiveness of near quasi-neutrality across the genome (mean net selection coefficients near zero, but with significant temporal variance about the mean, and little evidence of positive covariance of selection across time intervals); the preponderance of weak positive selection operating on minor alleles; and a genome-wide distribution of numerous small linkage islands of observable selection influencing levels of nucleotide diversity. These results suggest that interannual fluctuating selection is a major determinant of standing levels of variation in natural populations, challenge the conventional paradigm for interpreting patterns of nucleotide diversity and divergence, and motivate the need for the further development of theoretical expressions for the interpretation of population-genomic data.

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.

Fertilizer Effects on the Nitrogen Isotope Composition of Soil and Different Leaf Locations of Potted Camellia sinensis over a Growing Season.

The nitrogen-stable isotopes of plants can be used to verify the source of fertilizers, but the fertilizer uptake patterns in tea (Camellia sinensis) plants are unclear. In this study, potted tea plants were treated with three types of organic fertilizers (OFs), urea, and a control. The tea leaves were sampled over seven months from the top, middle, and base of the plants and analyzed for the δ15N and nitrogen content, along with the corresponding soil samples. The top tea leaves treated with the rapeseed cake OF had the highest δ15N values (up to 6.6‱), followed by the chicken manure, the cow manure, the control, and the urea fertilizer (6.5‱, 4.1‱, 2.2‱, and 0.6‱, respectively). The soil treated with cow manure had the highest δ15N values (6.0‱), followed by the chicken manure, rapeseed cake, control, and urea fertilizer (4.8‱, 4.0‱, 2.5‱, and 1.9‱, respectively). The tea leaves fertilized with rapeseed cake showed only slight δ15N value changes in autumn but increased significantly in early spring and then decreased in late spring, consistent with the delivery of a slow-release fertilizer. Meanwhile, the δ15N values of the top, middle, and basal leaves from the tea plants treated with the rapeseed cake treatment were consistently higher in early spring and lower in autumn and late spring, respectively. The urea and control samples had lower tea leaf δ15N values than the rapeseed cake-treated tea and showed a generalized decrease in the tea leaf δ15N values over time. The results clarify the temporal nitrogen patterns and isotope compositions of tea leaves treated with different fertilizer types and ensure that the δ15N tea leaf values can be used to authenticate the organic fertilizer methods across different harvest periods and leaf locations. The present results based on a pot experiment require further exploration in open agricultural soils in terms of the various potential fertilizer effects on the different variations of nitrogen isotope ratios in tea plants.

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.

Time series analysis of PM2.5 pollution risk based on the supply and demand of PM2.5 removal service: a case study of the urban areas of Beijing.

Demonstrating the temporal changes in PM2.5 pollution risk in regions facing serious PM2.5 pollution problems can provide scientific evidence for the air pollution control of the region. However, research on the variation of PM2.5 pollution risk on a fine temporal scale is very limited. Therefore, we developed a method for quantitative characterizing PM2.5 pollution risk based on the supply and demand of PM2.5 removal services, analyzed the time series characteristics of PM2.5 pollution risk, and explored the reasons for the temporal changes using the urban areas of Beijing as the case study area. The results show that the PM2.5 pollution risk in the urban areas of Beijing was close between 2008 and 2012, decreased by approximately 16.3% in 2016 compared to 2012, and further decreased by approximately 13.2% in 2021 compared to 2016. The temporal variation pattern of the PM2.5 pollution risk in 2016 and 2021 showed significant differences, including an increase in the number of risk-free days, a decrease in the number of heavily polluted days, and an increase in the stability of the risk day sequence. The significant reduction in risk level was mainly attributed to Beijing's air pollution control measures, supplemented by the impact of COVID-19 control measures in 2021. The results of PM2.5 pollution risk decomposition indicate that compared to the previous 2 years, the stability and predictability of the risk variation in 2016 increased, but the overall characteristics of high risk from November to February and low risk from April to September did not change. The high risk from November to February was mainly due to the demand for coal heating during this period, a decrease in PM2.5 removal service supply caused by plant leaf fall, and the common occurrence of temperature inversions in winter, which hinders the diffusion of air pollutants. This study provides a method for the analysis of PM2.5 pollution risk on fine temporal scales and may provide a reference for the PM2.5 pollution control in the urban areas of Beijing.

[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.

Temporal variations and chemical characteristics of marine PM2.5 at Dongsha Islands, South China Sea: Three-year measurement.

The study of long-range transport effects on marine fine particles (PM2.5), particularly in remote sites such as the Dongsha Islands, is pivotal for advancing our understanding of air pollution dynamics on a regional scale and for formulating effective environmental policies. PM2.5 concentrations were examined over three consecutive years and grouped based on their transport routes. The backward trajectory simulation revealed that high PM2.5 concentrations were observed in the West Channel, originating from North and Central China, the Korean Peninsula, and the Japanese Islands, opposed to the East Channel. High PM2.5 concentrations, commonly observed in winter and spring, were mainly attributed to the Asian Northeastern Monsoons. Water-soluble inorganic ions constituted the major components, accounting for 37.8-48.7% of PM2.5, and followed by metal elements (15.5-20.0%), carbons (7.5-13.3%), levoglucosan (0.01-0.17%), and organic aerosols (0.2-2.2%). Secondary inorganic aerosols as the dominant source accounted for 8.3-24.7% of PM2.5, while sea salts were the secondary major contributor. High levoglucosan contribution (3.8-7.2%) in winter and spring was attributed to biomass burning, mainly from the Indochina Peninsula. Chemical mass balance receptor modeling resolved that major sources of PM2.5 were secondary sulfate, sea salts, fugitive dust, and industrial boilers. This study concluded that the long-range transport of PM2.5 gradually increased since fall, contributing 52.1-74.3%, highlighting its substantial impact on PM2.5 in all seasons except summer.

The temporal variation in the effects of extreme temperature on respiratory mortality: Evidence from 136 cities in China, 2006-2019.

BACKGROUND: In the context of climate change and urbanization, the temporal variation of the adverse health effect of extreme temperature has attracted increasing attention. METHODS: The meteorological data and the daily death records of mortality from respiratory diseases of 136 Chinese cities were from 2006 to 2019. Heat wave and cold spell were selected as the indicator events of extreme high temperature and extreme low temperature, respectively. The generalized linear model and time-varying distributed lag model were used to perform a two-stage time-series analysis to evaluate the temporal variation of the mortality risk associated with extreme temperature in the total population, sub-populations (sex- and age- specific) and different regions (climatic zone and relative humidity level). RESULTS: During the study period, relative risk (RR) of respiratory mortality associated with heat wave decreased from 1.22 (95 %CI: 1.07-1.39) to 1.13 (95 %CI: 1.01-1.26) in the total population, and RR of respiratory mortality associated with cold spell decreased from 1.30 (95 %CI: 1.14-1.49) to 1.17 (95 %CI: 1.08-1.26). The impact of heat wave reduced in the males (P = 0.044) and in the females as with cold spell (P < 0.001). The respiratory mortality risk of people over 65 associated with cold spell decreased (P = 0.040 for people aged 65-74 and P < 0.001 for people over 75). The effect of cold spell reduced in cities from tropical or arid zone (P = 0.035). The effects of both heat wave and cold spell decreased in cities with the relative humidity in the first quartile (P = 0.046 and 0.010, respectively). CONCLUSION: The impact of heat wave on mortality of respiratory diseases decreased mainly in males and cities with the lowest relative humidity, while the impact of cold spell reduced in females, people over 65 and tropical and arid zone, suggesting adaptation to extreme temperature of Chinese residents to some extent.

Infestation prevalence, spatio-temporal distribution, phylogenetic positioning, and pathogen investigation of Argas persicus ticks in domestic hens (Gallus gallus domesticus) from Pakistan.

Soft ticks pose significant health risks as vectors of various pathogens. This study explored the spatio-temporal distribution and genetic relationships of the soft tick species Argas persicus infesting domestic hens (Gallus gallus domesticus) across different districts in Pakistan. An examination of 778 hens revealed a notable tick infestation prevalence of 70.82%, with a total of 1299 ticks collected from 551 hens. The overall mean intensity was 2.19 soft ticks per infested chicken, and the overall mean abundance was 1.61 soft ticks per examined hen. Morphological identification confirmed all collected ticks (n = 1210) as A. persicus, comprising 719 males, 333 females, 121 nymphs, and 38 larvae. The Haveli, Muzaffarabad, and Kotli districts had the highest infestation rates, while Bagh had the lowest. Molecular analyses of tick DNA, focusing on 16S rDNA and 12S rDNA sequences, revealed genetic similarities among A. persicus soft ticks from Pakistan and other regions, providing insights into their evolutionary history. Importantly, no Babesia, Rickettsia, or Anaplasma infections were detected in the examined samples. These findings enhance the understanding of soft tick infestation patterns and the genetic diversity of A. persicus in the studied region.

River Dust-Induced Air Pollution in a Changing Climate: A Study of Taiwan's Choshui and Kaoping Rivers.

This study investigates river dust episodes along the Choshui and Kaoping Rivers in Taiwan, focusing on their spatiotemporal distribution and correlation with hydrometeorological factors (temperature, precipitation, relative humidity, and wind speed). Using the Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN) algorithm and time-dependent intrinsic correlation (TDIC) analysis, we identified significant annual and diurnal correlations between PM10 concentrations and these factors. The analysis revealed that wind speed at Lunbei station had a positive annual correlation with PM10, while other factors exhibited significant negative correlations. Seasonal variations in PM10 correlations with temperature, relative humidity, and wind speed were observed, aligning with the prevailing seasons of river dust episodes. Wind motion analysis highlighted diurnal associations with land-sea breezes and annual correlations with the winter monsoon. Specifically, the Choshui River's dust events coincided with the northeast monsoon, whereas the Kaoping River's events occurred during the northwest and southwest monsoons. The study also uncovered that downstream stations (Lunbei and Daliao) were more prone to severe dust events than upstream stations (Douliu and Pingtung). These findings enhance our understanding of the dynamics and environmental impacts of river dust episodes, providing valuable insights for air quality management and health risk mitigation.

Temporal variability can promote migration between habitats.

Understanding the conditions that promote the evolution of migration is important in ecology and evolution. When environments are fixed and there is one most favorable site, migration to other sites lowers overall growth rate and is not favored. Here we ask, can environmental variability favor migration when there is one best site on average? Previous work suggests that the answer is yes, but a general and precise answer remained elusive. Here we establish new, rigorous inequalities to show (and use simulations to illustrate) how stochastic growth rate can increase with migration when fitness (dis)advantages fluctuate over time across sites. The effect of migration between sites on the overall stochastic growth rate depends on the difference in expected growth rates and the variance of the fluctuating difference in growth rates. When fluctuations (variance) are large, a population can benefit from bursts of higher growth in sites that are worse on average. Such bursts become more probable as the between-site variance increases. Our results apply to many (≥ 2) sites, and reveal an interplay between the length of paths between sites, the average differences in site-specific growth rates, and the size of fluctuations. Our findings have implications for evolutionary biology as they provide conditions for departure from the reduction principle, and for ecological dynamics: even when there are superior sites in a sea of poor habitats, variability and habitat quality across space determine the importance of migration.

Spatiotemporal variations and risk assessment of estrogens in the water of the southern Bohai Sea: A comprehensive investigation spanning three years.

The ubiquitous and adverse effects of estrogens have aroused global concerns. Natural and synthetic estrogens in 255 water samples from the southern Bohai Sea were analyzed over three years. Total estrogen concentrations were 11.0-268 ng/L in river water and 1.98-99.7 ng/L in seawater, with bisphenol A (BPA) and 17α-ethynylestradiol (EE2) being the predominant estrogens, respectively. Estrogen showed the highest concentrations in summer 2018, followed by spring 2021 and spring 2019, which was consistent with the higher estrogen flux from rivers during summer. Higher estrogen concentrations in 2021 than in 2019 were driven by the higher level of BPA, an additive used in personal protective equipment. Estrogen exhibited higher concentrations in the southern coast of the Yellow River Delta and the northeastern coast of Laizhou bay due to the riverine input and aquaculture. Estrogens could disturb the normal endocrine activities of organisms and edict high ecological risks (90th simulated RQT > 1.0) to aquatic organisms, especially to fish. EE2 was the main contributor of estrogenic potency and ecological risk, which requires special concern. This is the first comprehensive study of estrogen spatiotemporal variations and risks in the Bohai Sea, providing insights into the environmental behavior of estrogens in coastal regions.

Accountability analysis of health benefits related to National Action Plan on Air Pollution Prevention and Control in China.

China is one of the largest producers and consumers of coal in the world. The National Action Plan on Air Pollution Prevention and Control in China (2013-2017) particularly aimed to reduce emissions from coal combustion. Here, we show whether the acute health effects of PM2.5 changed from 2013 to 2018 and factors that might account for any observed changes in the Beijing-Tianjin-Hebei (BTH) and the surrounding areas where there were major reductions in PM2.5 concentrations. We used a two-stage analysis strategy, with a quasi-Poisson regression model and a random effects meta-analysis, to assess the effects of PM2.5 on mortality in the 47 counties of BTH. We found that the mean daily PM2.5 levels and the SO42- component ratio dramatically decreased in the study period, which was likely related to the control of coal emissions. Subsequently, the acute effects of PM2.5 were significantly decreased for total and circulatory mortality. A 10 µg/m3 increase in PM2.5 concentrations was associated with a 0.16% (95% CI: 0.08, 0.24%) and 0.02% (95% CI: -0.09, 0.13%) increase in mortality from 2013 to 2015 and from 2016 to 2018, respectively. The changes in air pollution sources or PM2.5 components appeared to have played a core role in reducing the health effects. The air pollution control measures implemented recently targeting coal emissions taken in China may have resulted in significant health benefits.

Spatial and temporal-trend assessment of desertification-sensitive land using the desertification sensitivity index in the provincial Ninh Thuan, Vietnam.

Desertification is a specific land-degrading process, reducing soil productivity and potentially threatening global food security. Therefore, spatially and temporally identifying and mapping desertification-sensitive areas is essential for better management. The current study aimed to (1) assess spatial areas sensitive to desertification and (2) examine the changing tendency of the desertification-sensitive areas over the past 25 years in the provincial Ninh Thuan. The desertification sensitivity index (DSI) was computed based on the Medalus model using 10 quantitative parameters, grouped into the soil, climate, and vegetation quality indexes, computed for the years 1996, 2005, 2010, and 2016. GIS was used to map desertification-sensitive areas associated with five DSI classes. Results showed that classes II and III had the highest area percentage, followed by classes IV and V, and class I. The classes most sensitive to desertification (classes IV and V) covered around 13 to 17%, and classes II and III were 25 to 32% of the total study area, respectively. The coastal areas located in the southeastern parts were more sensitive to desertification than the other parts. Over the four examined periods, the areas of classes IV and V increased while those of classes II and I decreased. These indicated that the study province tended to increase in its desertification sensitivity with a severe increase in the coastal areas over the past 25 years. The key factors involved in these changes could be related the human activities and climate variation, which could be more serious in southeastern areas than in the other areas.

Temporal dynamics of urban air pollutants and their correlation with associated meteorological parameters: an investigation in northern Indian cities.

This study delves into the intricate dynamics of air pollution in the rapidly expanding northern regions of India, examining the intertwined influences of agricultural burning, industrialization, and meteorological conditions. Through comprehensive analysis of key pollutants (PM2.5, PM10, NO2, SO2, CO, O3) across ten monitoring stations in Uttar Pradesh, Haryana, Delhi, and Punjab, a consistent pattern of high pollution levels emerges, particularly notable in Delhi. Varanasi leads in SO2 and O3 concentrations, while Moradabad stands out for CO levels, and Jalandhar for SO2 concentrations. The study further elucidates the regional distribution of pollutants, with Punjab receiving significant contributions from SW, SE, and NE directions, while Haryana and Delhi predominantly face air masses from SE and NE directions. Uttar Pradesh's pollution sources are primarily local, with additional inputs from various directions. Moreover, significant negative correlations (p < 0.05) between PM10, NO2, SO2, O3, and relative humidity (RH) underscore the pivotal role of meteorological factors in shaping pollutant levels. Strong positive correlations between PM2.5 and NO2 (0.71 to 0.93) suggest shared emission sources or similar atmospheric conditions in several cities. This comprehensive understanding highlights the urgent need for targeted mitigation strategies to address the multifaceted drivers of air pollution, ensuring the protection of public health and environmental sustainability across the region.

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.

Hourly land-use regression modeling for NO2 and PM2.5 in the Netherlands.

Annual average land-use regression (LUR) models have been widely used to assess spatial patterns of air pollution exposures. However, they fail to capture diurnal variability in air pollution and consequently might result in biased dynamic exposure assessments. In this study we aimed to model average hourly concentrations for two major pollutants, NO2 and PM2.5, for the Netherlands using the LUR algorithm. We modelled the spatial variation of average hourly concentrations for the years 2016-2019 combined, for two seasons, and for two weekday types. Two modelling approaches were used, supervised linear regression (SLR) and random forest (RF). The potential predictors included population, road, land use, satellite retrievals, and chemical transport model pollution estimates variables with different buffer sizes. We also temporally adjusted hourly concentrations from a 2019 annual model using the hourly monitoring data, to compare its performance with the hourly modelling approach. The results showed that hourly NO2 models performed overall well (5-fold cross validation R2 = 0.50-0.78), while the PM2.5 performed moderately (5-fold cross validation R2 = 0.24-0.62). Both for NO2 and PM2.5 the warm season models performed worse than the cold season ones, and the weekends' worse than weekdays'. The performance of the RF and SLR models was similar for both pollutants. For both SLR and RF, variables with larger buffer sizes representing variation in background concentrations, were selected more often in the weekend models compared to the weekdays, and in the warm season compared to the cold one. Temporal adjustment of annual average models performed overall worse than both modelling approaches (NO2 hourly R2 = 0.35-0.70; PM2.5 hourly R2 = 0.01-0.15). The difference in model performance and selection of variables across hours, seasons, and weekday types documents the benefit to develop independent hourly models when matching it to hourly time activity data.

Temporal variation of the PM2.5/PM10 ratio and its association with meteorological factors in a South American megacity: Metropolitan Area of Lima-Callao, Peru.

The Metropolitan Area of Lima-Callao (MALC) is a South American megacity that has suffered a serious deterioration in air quality due to high levels of particulate matter (PM2.5 and PM10). Studies on the behavior of the PM2.5/PM10 ratio and its temporal variability in relation to meteorological parameters are still very limited. The objective of this study was to analyze the temporal trends of the PM2.5/PM10 ratio, its temporal variability, and its association with meteorological variables over a period of 5 years (2015-2019). For this, the Theil-Sen estimator, bivariate polar plots, and correlation analysis were used. The regions of highest mean concentrations of PM2.5 and PM10 were identified at eastern Lima (ATE station-41.2 µg/m3) and southern Lima (VMT station-126.7 µg/m3), respectively. The lowest concentrations were recorded in downtown Lima (CDM station-16.8 µg/m3 and 34.0 µg/m3, respectively). The highest average PM2.5/PM10 ratio was found at the CDM station (0.55) and the lowest at the VMT station (0.27), indicating a predominance of emissions from the vehicular fleet within central Lima and a greater emission of coarse particles by resuspension in southern Lima. The temporal progression of the ratio of PM2.5/PM10 showed positive and highly significant trends in northern and central Lima with values of 0.03 and 0.1 units of PM2.5/PM10 per year, respectively. In the southern region of Lima, the trend was also significant, showcasing a value of 0.02 units of PM2.5/PM10 per year. At the hourly and monthly level, the PM2.5/PM10 ratio presented a negative and significant correlation with wind speed and air temperature, and a positive and significant correlation with relative humidity. These findings offer insights into identifying the sources of PM pollution and are useful for implementing regulations to reduce air emissions considering both anthropogenic sources and meteorological dispersion patterns.

Carbon reduction and water saving potentials for growing corrugated boxes for express delivery services in China.

Corrugated packaging for express grew by 90 times to 16.5 Mt y-1 in China, where 81% of recent global express delivery growth occurred. However, the environmental impacts of production, usage, disposal, and recycling of corrugated boxes under the entire supply chain remain unclear. Here, we estimate the magnitudes, drivers, and mitigation potentials of cradle-to-grave life-cycle carbon footprint (CF) and three colors of water footprints (WFs) for corrugated cardboard packaging in China. Over 2007 to 2021, CF, blue and gray WFs per unit package decreased by 45%, 60%, and 84%, respectively, while green WF increased by 23% with growing imports of virgin pulp and China's waste ban. National total CF and WFs were 21 to 102 folded with the scale effects. Only a combination of the supply chain reconstruction, lighter single-piece packaging, and increased recycling rate can possibly reduce the environmental footprints by 24 to 44% by 2035.