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This study employs an improved Carnegie-Ames-Stanford Approach (CASA) model to calculate the Net Primary Productivity (NPP) of Spartina alterniflora (SA) and various other land use/land cover types (LULC) across coastal China over multiple years. The research aims to provide significant theoretical and practical insights into carbon sink research in coastal zones, sustainable development, and resource management. Key findings include identifying the first εmax value of 2.219 g C/MJ for SA, addressing a critical data gap in CASA modeling research on invasive plants. SA's NPP exhibited higher values in Shanghai and Zhejiang due to factors such as genetic diversity, invasion duration, and tidal dynamics. In contrast, other LULC exhibited higher NPP values in southern and inland regions, characterized by greater vegetation cover and favorable growing conditions. In 2020, SA and other LULC sequestered 16.352 kt C and 0.821*106 kt C, respectively. From 2000 to 2020, the average annual NPP and total carbon storage of SA and other LULC increased significantly, primarily driven by Shanghai and deciduous needleleaf forests, respectively. Seasonal NPP trends followed summer> spring> autumn> winter, influenced by climate conditions and plant life activities. Economic assessments in 2020 estimated SA's carbon storage value at RMB0.409 billion (Market Value method) or RMB5.562 billion (Carbon Tax method), with RMB2.054 billion attributed to oxygen release values, underscoring its economic and ecological potential. Among other LULC, evergreen broadleaf forests showed the highest carbon storage value (RMB183.463 billion). The study emphasizes the critical role of all LULC in carbon storage and oxygen release, advocating for targeted conservation and land management strategies. It suggests that managing SA should balance stringent control in high-risk areas, lenient measures in low-risk areas, eradication of scattered populations, and maximizing ecological benefits in retention areas, with continuous monitoring and adaptive management strategies to balance conservation and development efforts.

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Plantation forests enhance carbon storage in terrestrial ecosystems in China. Larix kaempferi (Lamb.) Carrière (Lamb.) (Larix olgensis Henry) is the main species for afforestation in the eastern Liaoning Province. Therefore, it is important to understand the correlation between the site class and carbon sink potential of Larix kaempferi plantations in Liaoning Province for afforestation and carbon sink in this area. The model was fitted using three classical theoretical growth equations: the Richards model, the Korf model, and the Hossfeld model. This study used the forest resource inventory data for management in Liaoning Province in 2011 to build six dynamic height-age models for a Larix kaempferi plantation in Dandong City regardless of base-age. The optimal model derived by the generalized algebraic difference approach (GADA) method was compared with the model derived by the algebraic difference approach (ADA) method. The superiority of GADA was demonstrated by comparison. The Levenberg-Marquardt algorithm was used to fit the model. The statistical and biological characteristics were considered synthetically when comparing the models. The best model was screened out by statistical analysis and graphic analysis. The results show that the differential height-age model derived from Richards equation can well explain the growth process of Larix kaempferi in Dandong City, Liaoning Province under different conditions. The site index model based on Richards equation and derived by GADA was used to calculate the site class of a Larix kaempferi plantation in Dandong City. The net primary productivity (NPP) value from the past ten years was extracted from the MOD17A3HGF data set. Spearman correlation analysis and Kendall correlation analysis were used to show that there is a significant positive correlation between NPP value and site class of Larix kaempferi plantation in Dandong City. Among them, the highest growth occurred in 2016; NPP increased by about 3.914 gC/m2/year for every two increases in height-age grade; the lowest increase in NPP was in 2014; NPP increased by about 2.113 gC/m2/year for every two increases in height-age grade; and for every two increases in height-age grade in the recent ten years, the average NPP value increased by about 2.731 gC/m2/year.

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The main task of the site evaluation report for nuclear installation concerning nuclear safety is the protection of the public and environment from the radiological consequences of radioactive releases in both cases of normal operation and accident conditions. The atmosphere is the most important pathway to be considered in the assessment of the environmental impact of radioactive materials released from nuclear facilities. The present study is a comprehensive investigation of environmental assessments for the dose calculation model resulting from routine operation NPPs of 1000 Mw (e). The procedure consists of different parts, beginning with the manipulation of collected meteorological data such as wind speed and direction, temperature, incoming solar radiation and utilizing a set of empirical formulae for evaluating night solar radiation emitted from the ground, and then evaluating hourly stability classes and joint frequency distribution of winds by developing code. The dilution factor was evaluated using computer code (XOQDOQ code). Finally, the radiation dose assessments resulting from the routine operation of NPPs were calculated. The processes are adapted with IAEA recommendations documents, safety guides, and ICRP recommendations. The results reveal that there is no detectable value that affects the people surrounding the site nor the environmental area concerning low population zone or exclusion area.

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For the sustainable development of the city, in a study of Tianjin's rapid urbanization, we explore the complex interplay between land use change and the ecosystem carbon cycle from 2000 to 2020. Spatial analysis and profit-loss matrix calculations reveal contrasting ecological impacts: expansion of woodlands and grasslands enhances Net Primary Productivity (NPP) and reduces carbon emissions, while urban construction shows the opposite effect. Over 20 years, Tianjin's urban transformation led to a 16.91 GgC decline in NPP amidst a construction boom. However, post-2015 ecological policy shifts resulted in a significant net carbon uptake of 0.85 Gt, demonstrating the potential of policy interventions in mitigating environmental impacts of urbanization. This study underscores the importance of sustainable urban planning and ecological conservation strategies in highly urbanized settings.

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Nerve injury can not only lead to sensory and motor dysfunction, but also be complicated with neuropathic pain (NPP), which brings great psychosomatic injury to patients. At present, there is no effective treatment for NPP. Based on the functional characteristics of cell transplantation in nerve regeneration and injury repair, cell therapy has been used in the exploratory treatment of NPP and has become a promising treatment of NPP. In this article, we discuss the current mainstream cell types for the treatment of NPP, including Schwann cells, olfactory ensheathing cells, neural stem cells and mesenchymal stem cells in the treatment of NPP. These bioactive cells transplanted into the host have pharmacological properties of decreasing pain threshold and relieving NPP by exerting nutritional support, neuroprotection, immune regulation, promoting axonal regeneration, and remyelination. Cell transplantation can also change the microenvironment around the nerve injury, which is conducive to the survival of neurons. It can effectively relieve pain by repairing the injured nerve and rebuilding the nerve function. At present, some preclinical and clinical studies have shown that some encouraging results have been achieved in NPP treatment based on cell transplantation. Therefore, we discussed the feasible strategy of cell transplantation as a treatment of NPP and the problems and challenges that need to be solved in the current application of cell transplantation in NPP therapy.

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Spatialization and analysis of the gross domestic product of second and tertiary industries (GDP23) can effectively depict the socioeconomic status of regional development. However, existing studies mainly conduct GDP spatialization using nighttime light data; few studies specifically concentrated on the spatialization and analysis of GDP23 in a built-up area by combining multi-source remote sensing images. In this study, the NPP-VIIRS-like dataset and Sentinel-2 multi-spectral remote sensing images in six years were combined to precisely spatialize and analyze the variation patterns of the GDP23 in the built-up area of Zibo city, China. Sentinel-2 images and the random forest (RF) classification method based on PIE-Engine cloud platform were employed to extract built-up areas, in which the NPP-VIIRS-like dataset and comprehensive nighttime light index were used to indicate the nighttime light magnitudes to construct models to spatialize GDP23 and analyze their change patterns during the study period. The results found that (1) the RF classification method can accurately extract the built-up area with an overall accuracy higher than 0.90; the change patterns of built-up areas varied among districts and counties, with Yiyuan county being the only administrative region with an annual expansion rate of more than 1%. (2) The comprehensive nighttime light index is a viable indicator of GDP23 in the built-up area; the fitted model exhibited an R2 value of 0.82, and the overall relative errors of simulated GDP23 and statistical GDP23 were below 1%. (3) The year 2018 marked a significant turning point in the trajectory of GDP23 development in the study area; in 2018, Zhoucun district had the largest decrease in GDP23 at -52.36%. (4) GDP23 gradation results found that Zhangdian district exhibited the highest proportion of high GDP23 (>9%), while the proportions of low GDP23 regions in the remaining seven districts and counties all exceeded 60%. The innovation of this study is that the GDP23 in built-up areas were first precisely spatialized and analyzed using the NPP-VIIRS-like dataset and Sentinel-2 images. The findings of this study can serve as references for formulating improved city planning strategies and sustainable development policies.

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Herbaceous marshes are widely distributed in China and are vital to regional ecological security and sustainable development. Vegetation net primary productivity (NPP) is a vital indicator of vegetation growth. Climatic change can significantly affect NPP, but variations in NPP of herbaceous marsh and their responses to climate change in China remain unclear. Using meteorological data and MODIS NPP data during 2000-2020, this study analyzed the spatial and temporal variations of NPP and their responses to climate change in Chinese herbaceous marshes. We found that the annual NPP of herbaceous marshes in China increased significantly at a rate of 3.34 g C/m2/a from 2000 to 2020, with an average value of 336.60 g C/m2. The increased annual total precipitation enhanced the national average NPP, whereas annual mean temperature had no significant effect on the national average NPP. Regionally, precipitation had a significant positive effect on the NPP in temperate semi-arid and arid and temperate semi-humid and humid marsh regions. For the first time, we discovered asymmetry effects of daytime and nighttime temperatures on NPP in herbaceous marshes of China. In temperate humid and semi-humid marsh regions, increased summer daytime temperature decreased the NPP while increased summer nighttime temperature increased the NPP. In the Tibetan Plateau, increased autumn daytime temperature, as well as summer daytime and nighttime temperatures could increase the NPP of herbaceous marshes. This study highlights the different influences of seasonal climate change on the NPP of herbaceous marshes in China and indicates that the differential effects of daytime and nighttime temperatures should be considering in simulating the NPP of herbaceous marshes in terrestrial ecosystem models, especially under the background of global asymmetric diurnal warming.

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The study gives a morphofunctional assessment of the state of the thyroid gland of tundra voles (Microtus oeconomus Pall.) in conditions of an increased radiation background (the Ukhta district of the Komi Republic (Russia) and the 30-km zone of the Chernobyl NPP), as well as in an experiment with chronic external gamma irradiation in the low dose range. The work summarizes the experience of more than 35 years of field and laboratory research. The authors have noted the high sensitivity of the thyroid gland to chronic radiation against the general irradiation of the organism both in natural conditions and in the experiment. The repeatability of the observed effects in voles from natural populations and the comparability of some effects with the morphological changes occurring in animals after exposure to ionizing radiation in the experiment indicates the radiation nature of these effects. The tundra voles living in conditions of increased radiation background have been identified for a greater variety of morphological rearrangements in the thyroid parenchyma than the experimental animals. The complex and ambiguous nature of the thyroid gland responses to radiation exposure indicates the possibility of a significant increase in the risk of negative effects of ionizing radiation in contrast with the expected results of biological effects' extrapolation from high to low doses.

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Plot-scale experiments indicate that functional diversity (FD) plays a pivotal role in sustaining ecosystem functions such as net primary productivity (NPP). However, the relationships between functional diversity and NPP across larger scale under varying climatic conditions are sparsely studied, despite its significance for understanding forest-atmosphere interactions and informing policy development. Hence, we examine the relationships of community-weighted mean (CWM) and functional dispersion (FDis) of woody plant traits on NPP across China and if such relationships are modulated by climatic conditions at the national scale. Using comprehensive datasets of distribution, functional traits, and productivity for 9120 Chinese woody plant species, we evaluated the distribution pattern of community-weighted mean and functional dispersion (including three orthogonal trait indicators: plant size, leaf morphology, and flower duration) and its relationships with NPP. Finally, we tested the effects of climatic conditions on community-weighted mean/functional dispersion-NPP relationships. We first found overall functional diversity-NPP relationships, but also that the magnitude of these relationships was sensitive to climate, with plant size community-weighted mean promoting NPP in warm regions and plant size functional dispersion promoting NPP in wet regions. Second, warm and wet conditions indirectly increased NPP by its positive effects on community-weighted mean or functional dispersion, particularly through mean plant size and leaf morphology. Our study provides comprehensive evidence for the relationships between functional diversity and NPP under varying climates at a large scale. Importantly, our results indicate a broadening significance of multidimensional plant functional traits for woody vegetation NPP in response to rising temperatures and wetter climates. Restoration, reforestation actions and natural capital accounting need to carefully consider not only community-weighted mean and functional dispersion but also their interactions with climate, to predict how functional diversity may promote ecosystem functioning under future climatic conditions.

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This study aimed to investigate the vegetation production changes in Khuzestan province, Iran using MODIS data production, meteorological data, vegetation maps as well as topographic and field monitoring data in CASA model. The study area was divided into different climatic classes based on multivariate statistical method, so the vegetation of each climatic region was examined separately for changes in NPP values. Production changes due to degradation were calculated using the Miami model and subsequently, the rain use efficiency (RUE) and the light use efficiency (LUE) and correlation indices between the CASA model and ground data were determined. The results of this study (R2) showed that the accuracy of this model varies depending on the type of climatic regions (R2 = 0.80 to R2 = 0.15). In different climatic regions, the rate of NPP changes (very humid 68 gC/m2 to ultra-dry 15 gC/m2) varies in rangeland types. The highest rate of vegetation production is observed seasonally in May. Degradation conditions also reduced RUE and LUE. However, in hyper-arid regions, adaptations of plants in some different species (Hammada Spp.) increase their efficiency compared to other vegetation types. The results showed the importance of vegetation and climate classification in vegetation production studies.

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The emission reduction of atmospheric pollutants during the COVID-19 caused the change in aerosol concentration. However, there is a lack of research on the impact of changes in aerosol concentration on carbon sequestration potential. To reveal the impact mechanism of aerosols on rice carbon sequestration, the spatial differentiation characteristics of aerosol optical depth (AOD), gross primary productivity (GPP), net primary productivity (NPP), leaf area index (LAI), fraction of absorbed photosynthetically active radiation (FPAR), and meteorological factors were compared in the Sanjiang Plain. Pearson correlation analysis and geographic detector were used to analyze the main driving factors affecting the spatial heterogeneity of GPP and NPP. The study showed that the spatial distribution pattern of AOD in the rice-growing area during the epidemic was gradually decreasing from northeast to southwest with an overall decrease of 29.76%. Under the synergistic effect of multiple driving factors, both GPP and NPP increased by more than 5.0%, and the carbon sequestration capacity was improved. LAI and FPAR were the main driving factors for the spatial differentiation of rice GPP and NPP during the epidemic, followed by potential evapotranspiration and AOD. All interaction detection results showed a double-factor enhancement, which indicated that the effects of atmospheric environmental changes on rice primary productivity were the synergistic effect result of multiple factors, and AOD was the key factor that indirectly affected rice primary productivity. The synergistic effects between aerosol-radiation-meteorological factor-rice primary productivity in a typical temperate monsoon climate zone suitable for rice growth were studied, and the effects of changes in aerosol concentration on carbon sequestration potential were analyzed. The study can provide important references for the assessment of carbon sequestration potential in this climate zone.

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The potential establishment of a nuclear power plant (NPP) in the West Kalimantan is under consideration to address low electrification rates. This research assesses the demographic characteristics and population growth in the vicinity of the proposed NPP site in West Kalimantan. The investigation focuses on demographic aspects, population density, and anticipated growth during the operational phase of the reactor. The study area encompasses districts within a 25 km radius of the site. Employing a descriptive quantitative approach, this research integrates verified population data and utilizes spatial and temporal analysis. The exponential method is employed to forecast population growth throughout the operational lifespan of the reactor, to anticipate population increases in around NPP site. Reactor operation has potential radiological consequences. Results reveal that the suggested NPP site adheres to regulations concerning population density away from densely populated areas. Projected population growth is notable in specific regions, necessitating monitoring and policy attention. A gender-specific analysis highlights the higher female populations, with sensitivity to radiation exposure. These findings offer essential information for evaluating the radiological impact of the NPP on the population, so meeting NPP siting requirements. The study contributes valuable insights for decision-making regarding NPP development and safety considerations in West Kalimantan.

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Introduction: Economic loss estimation is critical for policymakers to craft policies that balance economic and health concerns during pandemic emergencies. However, this task is time-consuming and resource-intensive, posing challenges during emergencies. Method: To address this, we proposed using electricity consumption (EC) and nighttime lights (NTL) datasets to estimate the total, commercial, and industrial economic losses from COVID-19 lockdowns in the Philippines. Regression models were employed to establish the relationship of GDP with EC and NTL. Then, models using basic statistics and weather data were developed to estimate the counterfactual EC and NTL, from which counterfactual GDP was derived. The difference between the actual and the counterfactual GDP from 2020 to 2021 yielded economic loss. Results: This paper highlights three findings. First, the regression model results established that models based on EC (adj-R2 ≥ 0.978) were better at explaining GDP than models using NTL (adj-R2 ≥ 0.663); however, combining both EC and NTL improved the prediction (adj-R2 ≥ 0.979). Second, counterfactual EC and NTL could be estimated using models based on statistics and weather data explaining more than 81% of the pre-pandemic values. Last, the estimated total loss amounted to 2.9 trillion PhP in 2020 and 3.2 trillion PhP in 2021. More than two-thirds of the losses were in the commercial sector as it responded to both policies and the COVID-19 case surge. In contrast, the industrial sector was affected primarily by the lockdown implementation. Discussion: This method allowed monitoring of economic losses resulting from long-term and large-scale hazards such as the COVID-19 pandemic. These findings can serve as empirical evidence for advocating targeted strategies that balance public health and the economy during pandemic scenarios.

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Neuropathic pain (NPP) is a common type of chronic pain. Glial cells, including astrocytes (AS), are believed to play an important role in the progression of NPP. AS cells can be divided into various types based on their expression profiles, among which A1 and A2 types have clear functions. A1-type AS cells are neurotoxic, while A2-type AS cells exert neuroprotective functions. Some types of lysophosphatidic acid receptors (LPAR) have been shown to play a role in NPP. However, it remains unclear how AS cells and LPAR6 affect the occurrence and progression of NPP. In this study, we established a mouse model of chronic constriction injury (CCI) to simulate NPP. It was found that the expression of LPAR6 in AS cells of the spinal dorsal horn was increased in the CCI model, and the thresholds of mechanical and thermal pain were elevated after knocking out LPAR6, indicating that LPAR6 and AS cells participated in the occurrence of NPP. The experiment involved culturing primary AS cells and knocking down LPAR6 by Lentivirus. The results showed that the NF-κB signal pathway was activated and the number of A1-type AS cells increased in the CCI model. However, LPAR6 knockdown inhibited the NF-κB signal pathway and A1-type AS cells. The results of the mRNA sequencing and immunoprecipitation test indicate an interaction between LPAR6 and ROCK2. Inhibiting ROCK2 by Y-27632 increased mechanical and thermal pain thresholds and alleviated NPP at the molecular level. The study presents evidence that LPAR6 activates the NF-κB pathway through ROCK2 and contributes to the progression of NPP by increasing A1-type AS and decreasing A2-type AS. This suggests that LPAR6 could be a potential therapeutic target for alleviating NPP. Clinical applications that are successful can offer new therapeutic options, enhance the quality of life for patients, and potentially uncover new mechanisms for pain modulation.

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Ecto-nucleotide pyrophosphatase/phosphodiesterase 1 (NPP1) is an enzyme present in matrix vesicles (MV). NPP1 participates on the regulation of bone formation by producing pyrophosphate (PPi) from adenosine triphosphate (ATP). Here, we have used liposomes bearing dipalmitoylphosphatidylcholine (DPPC), sphingomyelin (SM), and cholesterol (Chol) harboring NPP1 to mimic the composition of MV lipid rafts to investigate ionic and lipidic influence on NPP1 activity and mineral propagation. Atomic force microscopy (AFM) revealed that DPPC-liposomes had spherical and smooth surface. The presence of SM and Chol elicited rough and smooth surface, respectively. NPP1 insertion produced protrusions in all the liposome surface. Maximum phosphodiesterase activity emerged at 0.082 M ionic strength, whereas maximum phosphomonohydrolase activity arose at low ionic strength. Phosphoserine-Calcium Phosphate Complex (PS-CPLX) and amorphous calcium-phosphate (ACP) induced mineral propagation in DPPC- and DPPC:SM-liposomes and in DPPC:Chol-liposomes, respectively. Mineral characterization revealed the presence of bands assigned to HAp in the mineral propagated by NPP1 harbored in DPPC-liposomes without nucleators or in DPPC:Chol-liposomes with ACP nucleators. These data show that studying how the ionic and lipidic environment affects NPP1 properties is important, especially for HAp obtained under controlled conditions in vitro.

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The investigation of the temporal-spatial characteristics and driving factors of vegetation ecosystem (VE) alterations held significant practical implications for the evaluation of the efficacy of rocky desertification management initiatives and safeguarding the ecological environment in the rocky desertification restoration region of Guizhou. We computed the comprehensive ecological quality index (Q) of vegetation based on the normalized difference vegetation index (NDVI) and net primary productivity (NPP). Combined with temperature, precipitation, sunshine duration, rocky desertification grade, land use, and the time series of various regions being included in national ecological functional zones, we analyzed the spatial-temporal distribution characteristics of VE changes and their response to climate change (CC) and ecological engineering (EE) by using partial derivative analysis method and scenario setting method in rocky desertification restoration areas in Guizhou. Results demonstrated that (1) the average values of NDVI, NPP, and Q all showed a fluctuating upward trend since 2000. Although the VE status of rocky desertification area was obviously worse than that of no rocky desertification area, it has a higher growth rate, especially the growth rates of NDVI, NPP, and Q in severe rocky desertification area were as high as 0.0050 year-1, 9.0733 g C m-2 year-1, and 0.7829 year-1, and the area with high recovery degree accounted for 93.19%, followed by the middle rocky desertification area. (2) CC was the main driving factor for NDVI and Q recovery, and EE was the main driving factor for NPP recovery. The contribution of EE to NPP and Q recovery increased with the increase of rocky desertification, as high as 82.13% and 30.31% in severe rocky desertification area. (3) The more serious the rocky desertification was, the more dependent the vegetation restoration was on ecological engineering, and the more difficult the restoration was. It was urgent to solve the ecological environmental problems. (4) EE played a greater role in the restoration of VE in the early stage of implementation. Its role gradually decreased in the later stages of implementation, while the role of CC increased. We provide a scientific basis for the follow-up treatment of rocky desertification, ecological environment restoration, and ecological protection effectiveness evaluation in Guizhou.

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Tropical vegetation plays a critical role in terrestrial carbon budget and supply many ecological functions such as carbon sequestration. In recent decades, India has witnessed an increase in net primary productivity (NPP), an important measure of carbon sequestration. However, uncertainties persist regarding the sustainability of these land carbon sinks in the face of climate change. The enhanced NPP is driven by the strong CO2 fertilization effect (CFE), but the temporal patterns of this feedback remain unclear. Using the carbon flux data from the Earth System Models (ESMs), an increasing trend in NPP was observed, with projections of NPP to 2.00 ± 0.12 PgCyr-1 (25 % increase) during 2021-2049, 2.36 ± 0.12 PgCyr-1 (18 % increase) during 2050-2079, and 2.67 ± 0.07 PgCyr-1 (13 % increase) during 2080-2099 in Indian vegetation under SSP585 scenario. This suggests a significant decline in the NPP growth rate. To understand the feedback mechanisms driving NPP, the relative effects of CFE and warming were analyzed. Comparing simulations from the biogeochemically coupled model (BGC) with the fully coupled model, the BGC model projected a 74.7 % increase in NPP, significantly higher than the 55.9 % increase projected by the fully coupled model by the end of the century. This indicates that the consistent increase in NPP was associated with CO2 fertilization. More importantly, results reveal that the decrease in the NPP growth rate was due to the declining contribution of CFE at a rate of -0.62 % per 100 ppm CO2 increase. This decline could be attributed to factors such as nutrient limitations and high temperatures. Additionally, significant shifts in the strength of carbon sinks in offsetting the CO2 emissions were identified, decreasing at a rate of -1.15 % per decade. This decline in the strength of vegetation carbon sequestration may increase the societal dependence on mitigation measures to address climate change.

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Human activity plays a crucial role in influencing PM2.5 concentration and can be assessed through nighttime light remote sensing. Therefore, it is important to investigate whether the nighttime light brightness can enhance the accuracy of PM2.5 simulation in different stages. Utilizing PM2.5 mobile monitoring data, this study introduces nighttime lighting brightness as an additional factor in the PM2.5 simulation model across various time periods. It compares the differences in simulation accuracy, explores the impact of nocturnal human activities on PM2.5 concentrations at different periods of the following day, and analyzes the spatial and temporal pollution pattern of PM2.5 in urban functional areas. The results show that (1) the incorporation of nighttime lighting brightness effectively enhances the model's accuracy (R2), with an improvement ranging from 0.04 to 0.12 for different periods ranges. (2) the model's accuracy improves more prominently during 8:00-12:00 on the following day, and less so during 12:00-18:00, as the PM2.5 from human activities during the night experiences a strong aggregation effect in the morning of the next day, with the effect on PM2.5 concentration declining after diffusion until the afternoon. (3) PM2.5 is primarily concentrated in urban functional areas including construction sites, roads, and industrial areas during each period. But in the period of 8:00-12:00, there is a significant level of PM2.5 pollution observed in commercial and residential areas, due to the human activities that occurred the previous night.

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To prevent the spread of COVID-19, China implemented large-scale lockdown measures in early 2020, resulting in a marked reduction in human activities over a short period. Studies have explored environmental changes during lockdowns, lacking analysis of response of net primary productivity (NPP) to lockdowns, especially for diverse vegetation types. Correlation between NPP and impact factors during lockdowns remains unclear. Through Google Earth Engine, we evaluated spatial-temporal changes in spring NPP at multiple scales during lockdown period (LD, 2020) compared with unlocked period (UL, 2017-2019) by remote sensing data in urban areas of China. Changes in four impact factors, aerosol optical depth (AOD) and photosynthetically active radiation (PAR) (via remote sensing data), alongside temperature (TEM) and precipitation (PRE) (via meteorological data) were explored. Additionally, geodetector, a valuable statistical tool for detecting the driving ability of various elements, was employed to explore the underlying causes of vegetation changes during LD. In the spring of LD: 1) National urban NPP generally increased (+6.50 %), notably in Northeast China (NE), North China (N) and East China (E). Besides, overall urban AOD decreased (-3.64 %), notably in N and Central China (C). National urban PAR increased (+2.7 %), particularly in C and Northwest China (NW). However, overall urban TEM (-0.06 %) and PRE (-1.21 %) changed negatively. 2) NPP in all three vegetation types in urban areas enhanced, with change rates: croplands > forests > grasslands. Evident enhancements occurred in the forests and croplands in N, and the grasslands in NE. 3) Through geodetector, during LD, AOD (q = 0.223) and TEM (q = 0.272) emerged as the dominant factors for NPP. Compared with UL, the explanatory power of AOD and PAR on NPP increased during LD. This study provides valuable insights into understanding the effects of short-term human activities on vegetation productivity, offering reference for the formulation of ecological and environmental policies.

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Global carbon emissions have exacerbated the greenhouse effect, exerting a profound impact on ecosystems worldwide. Gaining an understanding of the fluctuations in vegetation net primary productivity (NPP) is pivotal in the assessment of environmental quality, estimation of carbon source/sink potential, and facilitation of ecological restoration. Employing MODIS and meteorological data, we conducted a comprehensive analysis of NPP evolution in Chinese vegetation ecosystems (VESs), employing Theil-Sen median trend analysis and the Mann-Kendall test. Furthermore, utilizing scenario-based analysis, we quantitatively determined the respective contributions of climate change and land use change to NPP variations across various scales. The overall NPP exhibited a discernible upward trend from 2000 to 2020, with a growth rate of 5.83 gC·m-2·year-1. Forestland ecosystem (FES) displayed the highest rate of increase (9.40 gC·m-2·year-1), followed by cropland ecosystem (CES) (4.00 gC·m-2·year-1) and grassland ecosystem (GES) (3.40 gC·m-2·year-1). Geographically, NPP exhibited a spatial pattern characterized by elevated values in the southeast and diminished values in the northwest. In addition, climate change had elevated 76.39 % of CES NPP, 90.62 % of FES NPP, and 71.78 % of GES NPP. At the national level, climate change accounted for 83.14 % of the NPP changes, while land use change contributed 14.14 %. Notably, climate change emerged as the primary driving force behind NPP variations across all VEGs, with land use change exerting the most pronounced influence on CES. At the grid scale (2 km × 2 km), land use change played a substantial role in all VEGs, contributing 60.01 % in CES, 54.20 % in FES, and 55.61 % in GES of the NPP variations.