Urban green spaces (UGSs) are often positively associated with the health of urban residents. However, UGSs may also have adverse health effects by releasing biogenic volatile organic compounds (BVOCs) and increasing the ambient concentrations of ozone (O₃) and secondary organic aerosols in urban areas. BVOC emissions from UGSs might be underestimated because of the lack of consideration of the UGS land-use type in urban areas. As such, in this study, we used a newly released satellite dataset, Sentinel-2, with a resolution of 10 m, to derive the classification distribution of UGSs and predict the UGS emissions of BVOCs in Beijing in 2019. The results showed that the annual emissions of BVOCs from UGSs were approximately 2.9 Gg C (95% confidence interval (CI): 2.4–3.3) in the six core districts, accounting for approximately 39% of the total UGS emissions in Beijing. Compared with the results based on Sentinel-2, the BVOC emissions might be underestimated by approximately 37% (95% CI: 11–63) using the commonly used satellite dataset. UGSs produced the highest BVOC emissions in summer (from June to August), accounting for 75.2% of the annual emissions. UGSs contributed the most to the O₃ formation potential in summer, accounting for 41.5% of the total. We could attribute a considerable amount of the O₃ concentration (27.0 μg m⁻³, 95% CI: 21.4–32.6) to the UGS BVOCs produced in the core districts of Beijing in July. The new BVOC emissions dataset based on Sentinel-2 vegetation information facilitates modeling studies on the formation of surface O₃ in urban areas and assessments of the impact of UGSs on public health.

Vegetation plays an important role as both a sink of air pollutants via dry deposition and a source of biogenic VOC (BVOC) emissions which often provide the precursors of air pollutants. To identify the vegetation-driven offset between the deposition and formation of air pollutants, this study examines the responses of ozone and PM₂.₅ concentrations to changes in the leaf area index (LAI) over East Asia and its neighboring seas, using up-to-date satellite-derived LAI and green vegetation fraction (GVF) products. Two LAI scenarios that examine (1) table-prescribed LAI and GVF from 1992 to 1993 AVHRR and 2001 MODIS products and (2) reprocessed 2019 MODIS LAI and 2019 VIIRS GVF products were used in WRF-CMAQ modeling to simulate ozone and PM₂.₅ concentrations for June 2019. The use of up-to-date LAI and GVF products resulted in monthly mean LAI differences ranging from −56.20% to 96.81% over the study domain. The increase in LAI resulted in the differences in hourly mean ozone and PM₂.₅ concentrations over inland areas ranging from 0.27 ppbV to −7.17 ppbV and 0.89 μg/m³ to −2.65 μg/m³, and the differences of those over the adjacent sea surface ranging from 0.69 ppbV to −2.86 ppbV and 3.41 μg/m³ to −7.47 μg/m³. The decreases in inland ozone and PM₂.₅ concentrations were mainly the results of dry deposition accelerated by increases in LAI, which outweighed the ozone and PM₂.₅ formations via BVOC-driven chemistry. Some inland regions showed further decreases in PM₂.₅ concentrations due to reduced reactions of PM₂.₅ precursors with hydroxyl radicals depleted by BVOCs. The reductions in sea surface ozone and PM₂.₅ concentrations were accompanied by the reductions in those in upwind inland regions, which led to less ozone and PM₂.₅ inflows. The results suggest the importance of the selective use of vegetation parameters for air quality modeling.

Delay sowing of wheat is a common problem in Punjab that exacerbates serious yield loss. To reduce yield loss and improve yield, different combinations of foliar-applied bioregulator and micronutrients, control (CK), zinc (Zn), boron (B), thiourea (TU), Zn + B (ZnB), Zn + TU (ZnTU), B + TU (BTU), Zn + B + TU (ZnBTU) were applied at booting and grain filling stages in delayed sown wheat in 2017–18 and 2018–19. The results showed that ZnBTU treatment significantly increased leaf area index by 25.06% and 23.21%, spike length by 15.11% and 19.65% in 2017 and 2018, respectively, compared to CK. The ZnBTU treatment also increased 1000-grain weight by 21.96% and 22.01% in 2017 and 2018, respectively, compared to CK. Similarly, higher Zn, B and N contents in straw and grain were recoded for ZnBTU treatment which was statistically similar to ZnB and ZnTU treatments. Overall, ZnBTU treatment also increased the photosynthetic rate, transpiration rate, stomatal conductance by 46.67%, 26.03%, 76.25% and decreased internal CO₂ by 28.18%, compared to CK, respectively. Moreover, ZnBTU also recorded the highest grain yield in 2017–18 (25.05%) and 2018–19 (28.49%) than CK. In conclusion, foliar application of ZnBTU at the booting and grain filling stages of delayed sown wheat could be a promising strategy to increase grain yield.

Ecosystem water use efficiency (eWUE), defined as the ratio between carbon gains and water loss from the system, has been recognized as an important characteristic of carbon and water balances. The long-lasting “Grain for Green” Program (GFGP) initiated in 1999 in China’s Loess Plateau (CLP) is a large-scale ecological program in the world, which aims to improve the CLP’s ecosystem resilience by enhancing vegetation cover and productivity. Understanding how the GFGP can affect eWUE is imperative to ensuring sustainable water resources and to promoting sustainable management strategies. In this study, we evaluated the spatiotemporal variability of growing-season eWUE and examined its response to both climate change and vegetation coverage from 1982 to 2017. Our results indicate that growing-season eWUE, gross primary productivity (GPP), and evapotranspiration (ET) in CLP area increased significantly from 1982 to 2017. Specifically, eWUE, GPP, and ET increased more rapidly after China established the program. The most significant growth area of eWUE was found in main areas conducting GFGP project, including the Loess hilly and gully area (LHGA). Spatially, eWUE, GPP, and ET in the growing season increased from northwest to southeast, and higher eWUE was found in areas with high vegetation cover. The spatial and temporal variability of eWUE was related to vegetation cover (expressed as leaf area index, LAI) and climatic variability. Significant positive correlations were observed between growing-season LAI, temperature, and eWUE, because the LAI and temperature have a greater effect on photosynthesis than ET. Our results suggested that the GFGP was the main driving force that causes the spatial-temporal variability of eWUE in CLP.

The efficiency of nitrogenous fertilizers in South Asia is on a declining trajectory due to increased losses. Biochar (BC) and slow-releasing nitrogen fertilizers (SRNF) have been found to improve nitrogen use efficiency (NUE) in certain cases. However, field-scale studies to explore the potential of BC and SRNF in south Asian arid climate are lacking. Here we conducted a field experiment in the arid environment to demonstrate the response of BC and SRNF on cotton growth and yield quality. The treatments were comprised of two factors, (A) nitrogen sources, (i) simple urea, (ii)neem-coated urea, (iii)sulfur-coated urea, (iv) bacterial coated urea, and cotton stalks biochar impregnated with simple urea, and (B) nitrogen application rates, N₁=160 kg ha⁻¹, N₂ = 120 kg ha⁻¹, and N₃ = 80 kg ha⁻¹. Different SRNF differentially affected cotton growth, morphological and physiological attributes, and seed cotton yield (SCY). The bacterial coated urea at the highest rate of N application (160 kg ha⁻¹) resulted in a higher net leaf photosynthetic rate (32.8 μmol m⁻² s⁻¹), leaf transpiration rate (8.10 mmol s⁻¹), and stomatal conductance (0.502 mol m⁻² s⁻¹), while leaf area index (LAI), crop growth rate (CGR), and seed cotton yield (4513 kg ha⁻¹) were increased by bacterial coated urea at 120 kg ha⁻¹ than simple urea. However, low rate N application (80 kg ha⁻¹) of bacterial coated urea showed higher nitrogen use efficiency (39.6 kg SCY kg⁻¹ N). The fiber quality (fiber length, fiber strength, ginning outturn, fiber index, and seed index) was also increased with the high N application rates than N2 and N3 application. To summarize, the bacterial coated urea with recommended N (160 kg ha⁻¹) and 75% of recommended N application (120 kg ha⁻¹) may be recommended for farmers in the arid climatic conditions of Punjab to enhance the seed cotton yield, thereby reducing nitrogen losses.

The phytoremediation potential and enzymatic defense of a medicinal herb Leucas aspera was studied in the crude oil contaminated soil. The productivity, antioxidants, and phytochemical and functional group profiles of the plant species in stress conditions were investigated. Besides, changes in enzymes, beneficial bacterial population, and physico-chemical and total oil and grease (TOG) profiles in the contaminated soil were also studied. The results showed improvement in physico-chemical conditions, increase in beneficial bacterial population (4.1–5.4 folds), and decrease in TOG (31.3%) level of the contaminated soil by end of the experimental trials. The L. aspera treated contaminated soil showed enhancement in dehydrogenase (32.3%), urease (102.8%), alkaline phosphatase (174.4%), catalase (68.5%), amylase (76.16%), and cellulase (23.6%) activities by end of the experimental trials. Furthermore, there were significant variations in leaf area index, chlorophyll, and biomass contents of the experimental plant as against the initial level and control. Besides, the significant reduction in IC₅₀ values (24–27.4%) of L. aspera samples grown in contaminated soil confirms the strong antioxidant enzymatic defense of the plant species against the crude oil associated abiotic stress. The Fourier-transform infrared (FT-IR) analysis confirmed the uptake and metabolism of aliphatic hydrocarbons, aldehydes, alkyl halides, and nitro compounds by the experimental plant from the contaminated soil.

Future climate scenarios are predicting considerable threats to sustainable maize production in arid and semi-arid regions. These adverse impacts can be minimized by adopting modern agricultural tools to assess and develop successful adaptation practices. A multi-model approach (climate and crop) was used to assess the impacts and uncertainties of climate change on maize crop. An extensive field study was conducted to explore the temporal thermal variations on maize hybrids grown at farmer’s fields for ten sowing dates during two consecutive growing years. Data about phenology, morphology, biomass development, and yield were recorded by adopting standard procedures and protocols. The CSM-CERES, APSIM, and CSM-IXIM-Maize models were calibrated and evaluated. Five GCMs among 29 were selected based on classification into different groups and uncertainty to predict climatic changes in the future. The results predicted that there would be a rise in temperature (1.57–3.29 °C) during the maize growing season in five General Circulation Models (GCMs) by using RCP 8.5 scenarios for the mid-century (2040–2069) as compared with the baseline (1980–2015). The CERES-Maize and APSIM-Maize model showed lower root mean square error values (2.78 and 5.41), higher d-index (0.85 and 0.87) along reliable R² (0.89 and 0.89), respectively for days to anthesis and maturity, while the CSM-IXIM-Maize model performed well for growth parameters (leaf area index, total dry matter) and yield with reasonably good statistical indices. The CSM-IXIM-Maize model performed well for all hybrids during both years whereas climate models, NorESM1-M and IPSL-CM5A-MR, showed less uncertain results for climate change impacts. Maize models along GCMs predicted a reduction in yield (8–55%) than baseline. Maize crop may face a high yield decline that could be overcome by modifying the sowing dates and fertilizer (fertigation) and heat and drought-tolerant hybrids.

The spread of highly pathogenic avian influenza H5N1 has posed a major threat to global public health. Understanding the spatiotemporal outbreak characteristics and environmental factors of H5N1 outbreaks is of great significance for the establishment of effective prevention and control systems. The time and location of H5N1 outbreaks in poultry and wild birds officially confirmed by the World Organization for Animal Health from 2005 to 2019 were collected. Spatial autocorrelation analysis and multidistance spatial agglomeration analysis methods were used to analyze the global outbreak sites of H5N1. Combined with remote sensing data, the correlation between H5N1 outbreaks and environmental factors was analyzed using binary logistic regression methods. We analyzed the correlation between the H5N1 outbreak and environmental factors and finally made a risk prediction for the global H5N1 outbreaks. The results show that the peak of the H5N1 outbreaks occurs in winter and spring. H5N1 outbreaks exhibit aggregation, and a weak aggregation phenomenon is noted on the scale close to 5000 km. Water distance, road distance, railway distance, wind speed, leaf area index (LAI), and specific humidity were protective factors for the outbreak of H5N1, and the odds ratio (OR) were 0.985, 0.989, 0.995, 0.717, 0.832, and 0.935, respectively. Temperature was a risk factor with an OR of 1.073. The significance of these ORs was greater than 95%. The global risk prediction map was obtained. Given that the novel coronavirus (COVID-19) is spreading globally, the methods and results of this study can provide a reference for studying the spread of COVID-19.

With high groundwater levels, coal–grain overlap areas (CGOAs) are vulnerable to subsidence and water logging during mining activities, thereby impacting crop yields adversely. Such damage requires full reports of disturbed boundaries for agricultural reimbursement and ongoing reclamation, but because direct measurements are difficult in such cases because of vast unreachable areas, it is necessary to be able to identify out-of-production boundaries (OBs) and reduced-production boundaries (RBs) in the corresponding region. In this study, an OB was extracted by setting a threshold via the characteristics of the cultivated-land elevation based on a digital surface model and a digital orthophoto map generated using an unmanned aerial vehicle (UAV). Meanwhile, the above-ground biomass (AGB), the soil plant analysis development (SPAD) value of chlorophyll contents, and leaf area index (LAI) were used to select the appropriate vegetation indices (VIs) to produce a reduced-production map (RM) based on power regression (PR), exponential regression (ER), multiple linear regression (MR), and random forest (RF) algorithms. Finally, an improved Otsu segmentation algorithm was used to extract mild and severe RBs. The results showed the following. (1) Crop growth heights in a typical ponding basin of the CGOA rendered a fast and efficient approach to distinguishing the OB. (2) In subsequent sample modeling, the red-edge microwave VI (MVIᵣₑdgₑ), the normalized difference VI (NDVI), and the red-edge modified simple ratio index (MSRᵣₑdgₑ) combined with RF were shown to be optimal estimators for AGB (R² = 0.83, RMSE = 0.114 kg·m⁻²); the red-edge NDVI (NDVIᵣₑdgₑ), the green NDVI (GNDVI), and the red-edge chlorophyll index (CIᵣₑdgₑ) acted as strong tools in SPAD prediction using RF (R² = 0.83, RMSE = 0.152 SPAD); the red-edge modified simple ratio index (MSRᵣₑdgₑ), the GNDVI, and the green chlorophyll index (CIgᵣₑₑₙ) via MR were more accurate when conducting the inversion of LAI (R² = 0.88, RMSE = 1.070). (3) With the improved Otsu algorithm, multiple degrees of RB extraction can be achieved in RM. This study provides reference methods and theoretical support for determining disturbed boundaries in CGOAs with high groundwater levels for further agricultural compensation and reclamation processes.

The increased growth of vegetation has the potential to slow global climate warming. Therefore, analyzing and predicting the response assessment of Chinese vegetation to climate change is of great significance to studies of global warming. In this paper, we examine the spatiotemporal dynamics of vegetation leaf area index (LAI) values in China from 1981 to 2017 and their correlations with meteorological (hydrothermal) factors based on trend analysis and correlation analysis. We further construct an LAI prediction model based on hydrothermal conditions. The climate data obtained under different scenarios in the CMIP5 and CMIP6 climate models were used to predict the dynamic change trend of vegetation LAI from 2021 to 2100. The results show that most areas of China (72.82%) showed an improving trend in vegetation LAI from 1981 to 2017, during which the annual average LAI value increased at a rate of 0.0029 year⁻¹. Vegetation LAI in China was significantly correlated with climatic factors (temperature, precipitation, and evapotranspiration), and the LAI prediction model constructed based on hydrothermal conditions had a high accuracy (Pearson’s Cor value is 0.9729). From 2021 to 2100, approximately 2/3 of China’s vegetation LAI area showed an improvement trend, and the impact of climate change on vegetation LAI predictions under the high emission scenario was greater than that under the low emission scenario. This research can provide a basis for studies on the climatic drivers of vegetation change and the global vegetation dynamic model.