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Improved air quality leads to enhanced vegetation growth during the COVID–19 lockdown in India
Abstract
The direct effect of pandemic induced lockdown (LD) on environment is widely explored, but its secondary impacts remain largely unexplored. Therefore, we assess the response of surface greenness and photosynthetic activity to the LD-induced improvement of air quality in India. Our analysis reveals a significant improvement in air quality marked by reduced levels of aerosols (AOD, -19.27%) and Particulate Matter (PM 2.5, -23%) during LD (2020) LD (2020) from pre-LD (March-September months for the period 2017-2019). The vegetation exhibit positive response reflected by increase in surface greenness [Enhanced Vegetation Index (EVI, +10.4%)] and photosynthetic activity [Solar Induced Florescence (SiF, +11%)] during LD from pre-LD that coincides with two major agricultural seasons of India; Zaid (March-May) and Kharif (June-September). In addition, the croplands show a higher response [two-fold in EVI (14.45%) and four-fold in SiF (17.7%)] than that of forests. The prolonged growing period (phenology) and high rate of photosynthesis (intensification) led to the enhanced greening during LD owing to reduced pollution. This study, therefore, provides new insights into the response of vegetation to the improved air quality, which would give ideas to counter the challenges of food security in the context of climate pollution, and combat global warming by more greening.
... K Fig. 2 Detailed methodology flowchart representing overall steps followed in this study data/dataprod/mod12.php) land use class at 500 m spatial resolution to understand the land dynamics in coastal cities. The data for the land use class from MODIS have been used in various studies (Kashyap et al. 2023;Kuttippurath et al. 2023). In addition, elevation data are taken from Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) (https://asterweb.jpl.nasa.gov/gdem. ...
... K Fig. 2 Detailed methodology flowchart representing overall steps followed in this study data/dataprod/mod12.php) land use class at 500 m spatial resolution to understand the land dynamics in coastal cities. The data for the land use class from MODIS have been used in various studies (Kashyap et al. 2023;Kuttippurath et al. 2023). In addition, elevation data are taken from Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) (https://asterweb.jpl.nasa.gov/gdem. ...
The coastal regions of India have a high population density and are ecologically productive. However, they are also susceptible to both human activity and natural calamities, which can lead to erosion and accretion. As part of the sustainable management of coastal zones, these threats have taken precedence in evaluating shoreline dynamicity. This study demonstrated the effectiveness of integrating remote sensing and geographic information systems for comprehensive long-term coastal change analyses. The analysis reveals that the mean erosion rate along the Chennai coast ranges from −0.2 to −2.5 m/year. Accretion is also recorded along certain parts of the Chennai coast, with rates ranging from 1 to 4.6 m/year. The Vishakhapatnam shoreline has a consistent pattern of both erosion and accretion, with erosion rates ranging from −0.1 to −6.8 m/year and accretion from 0.2 to 5 m/year. However, most of the Puri coast exhibits an accretion pattern, with values ranging from approximately 0.1 to 3.22 m/year. The fluctuations in shorelines of these three metropolises are a matter of great concern, given that these coastal cities play a substantial part in India’s economic and cultural endeavors. The ongoing occurrence of climate change and global warming has led to an elevation in the worldwide sea level, along with a heightened intensity and frequency of extreme occurrences like tropical cyclones in the Bay of Bengal, where these three coasts are situated. The coastlines of these urban areas may experience alterations due to natural phenomena like rising sea levels and tropical cyclones, as well as a diverse array of human activity. This study may help to facilitate the formulation of suitable management strategies and regulations for the coastal areas of Vishakhapatnam, Puri, Chennai, and other Indian coastal places that have similar physical attributes.
... This creates difficulties in obtaining a comprehensive picture of the regional or global distribution of air pollutants [7], [8]. Additionally, the analytical tools available at that time often required complex and time-consuming procedures, limiting the ability of researchers and policymakers to make rapid, data-driven decisions [9]. This research challenge addresses the severe impacts caused by air pollution on a large scale, including complex phenomena such as smog and industrial air pollution. ...
This research aims to improve performance of air quality monitoring and understand the latest relevant technological developments. Employing the Kitchenham systematic literature review (SLR) method, the study examines 436 journal articles and conference proceedings published from 2019 to 2023, sourced from the Web of Science (WoS) and Scopus databases. The analysis was carried out using Leximancer 5.0 and identified research five themes; i) air quality, ii) artificial intelligence (AI), iii) pollution, iv) middleware, and v) smart environment. The results showed that only 48 journals had strict inclusion and exclusion criteria include relevance to the research theme, methodological quality, and contribution to the research field. In addition, this research integrates AI and middleware, which has significantly contributed to improving air quality. These findings can become the basis for the development of air quality monitoring technology that is more sophisticated and responsive to environmental needs. This research contributes to further understanding air quality monitoring technology trends and designing solutions to improve overall air quality.
... The relationship between PM10 levels and vegetation health underscores the far-reaching consequences of air pollution control measures, suggesting that policies aimed at enhancing air quality benefit not only human health but also significantly contribute to ecosystem recovery in mining-affected regions (Chu et al., 2023). However, it is essential to recognize that relationship between particulate matter and vegetation indices is complex (Kashyap et al., 2023). While increase in kNDVI associated with the decline in PM10 levels is promising, further research is necessary to disentangle potential confounding effects of leaf surface dust retention on vegetation index monitoring (Kayet et al., 2024;Yu et al., 2022). ...
Rare earth mining, essential for modern industries and economic growth, often leads to severe environmental degradation. Previous research has explored the ecological impacts of rare earth mining but has not fully investigated the intricate interplay and subdivisions of environmental and anthropogenic factors driving vegetation changes over extended periods. This study addresses this gap by employing time series remote sensing and SHAP-enhanced machine learning to analyze vegetation dynamics in China's Ganzhou rare earth mining area from 2001 to 2020. Using the kNDVI derived from Landsat data, we identified three distinct vegetation trajectory types: pro-environment, des-environment, and res-environment. An ensemble machine learning model combined with SHAP analysis revealed the cropland area proportion, PM10 levels, and shrubland area proportion as the most influential factors affecting vegetation across all mining types. Additionally, after 2012, the palmer drought severity index and downward surface shortwave radiation emerged as positive contributors to vegetation health, while population pressure had a more substantial negative influence in des-environment areas. Our findings highlight spatial heterogeneity in vegetation recovery patterns and highlight the complex interactions among land cover changes, air quality, climate factors, and human activities in shaping vegetation dynamics. This study provides valuable insights for developing targeted, context-specific restoration strategies in rare earth mining areas, contributing to more sustainable mining practices and global environmental management.
... Thirdly, this study mainly focused on variations in C leaf of marsh during the leaf green-up period on the TP, along with the impacts of precipitation and temperature. It is worth noting that other factors such as solar radiation, soil moisture, relative humidity, phenological change, plant type, leaf thicknesses, and human activity could also significantly influence the C leaf of marsh (Kashyap, Pandey, and Kuttippurath 2022, 2023a, 2023b. In the future, the effects of these factors on C leaf throughout the growth stages of marsh vegetation should be further explored. ...
Using MODIS leaf area index and meteorological data from 2000 to 2020, this study analyzed the variations in carbon allocation to leaves (Cleaf) of marsh during the leaf green-up period and their response to climate change across the TP based on partial correlation and linear regression analysis methods. The regionally averaged Cleaf of marsh showed an increasing trend during the leaf green-up period from 2000 to 2020. Diurnal warming has asymmetric effects on Cleaf of marsh during different stages of the leaf green-up period. During the early leaf green-up period, warming preseason daytime temperature significantly reduced Cleaf in the southwestern region, while warming preseason daytime and night-time temperatures increased Cleaf in the central region. During the late leaf green-up period, preseason temperatures exerted significantly positive and negative effects on Cleaf in the southwestern and central regions, respectively. During the early leaf green-up period, preseason precipitation increases significantly enhanced Cleaf in the southwestern region, but decreased Cleaf in the central region. This study highlights the distinct impacts of climatic change on Cleaf during different stages of the leaf green-up period and indicates that the asymmetric effects of diurnal warming should be considered in simulations of marsh carbon allocation in the future.
... Also, the findings are based on the specific datasets and statistical methods, which might vary from other studies that employ different datasets and statistical approach. Owing to the uncertainty of CCI estimates in the earlier decades (Dorigo et al. 2017), availability of MODIS satellite data since 2000 and the effects of COVID-induced lockdown on the terrestrial ecosystems in India (Kashyap et al. 2023b), our study period is restricted to 2000-2019. ...
Soil moisture (SM) interconnects various components of the Earth system and drives the land–atmosphere feedbacks and food production. However, around 40% of global vegetated land experiences SM drying. India is one of the global hotspots of land–atmosphere interactions and an extensively agrarian economy, but underexplored in terms of SM dynamics and its ramifications on food security. Here, we examine the mechanism of SM drying and its implications on cropland productivity in India based on remote sensing measurements and land surface model simulations in recent decades (2000–2019) and future projection of the 21st century. We find SM reduction predominantly in monsoon (4.5%) and winter (3%) seasons that are in the major agricultural seasons of Kharif and Rabi, respectively. Machine learning (ML)-based random forest (RF) reveals that temperature (T, 30.76%) is the dominant driver of SM variability, and then precipitation (P, 26.34%), evapotranspiration (ET, 26.08%) and surface greenness (16.82%). Concurrently, India experiences severe warming in terms of land (0.59 ℃/dec), soil (0.48 ℃/dec) and soil heat flux (SHF, 0.16 W/m²/dec) during 2000–2019. Partial correlation analysis between SM and T limiting the influence of P reveals a strong negative (> − 0.5) relationship in the agriculture intensive regions of Indo-Gangetic Plain (IGP) and South India (SI). Drying owing to warming and increased SHF, termed as warming-induced moisture stress, reduces gross primary productivity (GPP) (i.e. browning) and yield of major food crops of wheat, rain-fed rice, maize and soyabean, predominantly in SI and eastern IGP. Granger Causality shows that warming-induced soil moisture stress has a maximum temporal lag of 1 month. In a warming world, the ever-growing population demands more food, and therefore, the warming-induced soil moisture stress is a serious threat to food security in India and similar agro-climatic regions of the world. This calls for climate-resilient agriculture, better agronomic management, improved irrigation and adoption of water-efficient crops.
... Previous studies have verified that climatic and anthropogenic factors are influencing urban vegetation growth . In urbanized regions, enhanced vegetation growth could be attributed to factors such as climatic context , urban microclimate , and atmospheric chemistry (Kashyap et al., 2023;Zipper et al., 2016), as well as the effects of urban greening practices like irrigation and fertilization (Jia et al., 2018). Additionally, research has shown a close relationship between regional economic development and residential demand with urban vegetation growth (Peng et al., 2014;Zhang et al., 2021). ...
Urban vegetation is essential for the quality of life in cities. Despite direct vegetation loss during urban expansion, urbanization can indirectly enhance vegetation greening through various factors. Yet, it remains unclear what conditions promoted these greening trends within cities. We quantified the greenness trends in 294 Chinese cities based on satellite imagery (2001-2018), which we then explained with climate and socioeconomic indicators, particularly considering the National Garden Cities incentive program for urban greening (NGC). Results reveal large potential for enhancing greenness under urban densification, with larger cities leading urban greening development. We further show that the effectiveness of NGC in promoting enhanced urban greenness is context-dependent, particularly depending on aridity, which is not sufficiently considered in current policy. Our findings show that the indirect vegetation growth index is an effective tool to evaluate urban greening gover-nance and highlight the importance of tailoring regional greening strategies to local conditions for sustainable urban vegetation development.
... In such cases, we could use alternative reference points by using air-quality data or national holidays to distinguish between clean and polluted days. Recent efforts have been made to use the year 2020, during the COVID-19 pandemic, as a ref erence to observe that China and India appear greener in 2020 due to decreased AOD, though separating meteorological effects can be challenging (65,66). By understanding the complex rela tionships between human activity, air quality, and ecosystem productivity, we can develop more effective policies and practices to mitigate the negative effects of air pollution and enhance ecosystem productivity. ...
Aerosols can affect photosynthesis through radiative perturbations such as scattering and absorbing solar radiation. This biophysical impact has been widely studied using field measurements, but the sign and magnitude at continental scales remain uncertain. Solar-induced fluorescence (SIF), emitted by chlorophyll, strongly correlates with photosynthesis. With recent advancements in Earth observation satellites, we leverage SIF observations from the Tropospheric Monitoring Instrument (TROPOMI) with unprecedented spatial resolution and near-daily global coverage, to investigate the impact of aerosols on photosynthesis. Our analysis reveals that on weekends when there is more plant-available sunlight due to less particulate pollution, 64% of regions across Europe show increased SIF, indicating more photosynthesis. Moreover, we find a widespread negative relationship between SIF and aerosol loading across Europe. This suggests the possible reduction in photosynthesis as aerosol levels increase, particularly in ecosystems limited by light availability. By considering two plausible scenarios of improved air quality—reducing aerosol levels to the weekly minimum 3-d values and levels observed during the COVID-19 period—we estimate a potential of 41 to 50 Mt net additional annual CO 2 uptake by terrestrial ecosystems in Europe. This work assesses human impacts on photosynthesis via aerosol pollution at continental scales using satellite observations. Our results highlight i) the use of spatiotemporal variations in satellite SIF to estimate the human impacts on photosynthesis and ii) the potential of reducing particulate pollution to enhance ecosystem productivity.
... As there can be other drives, this assessment does not claim exact contribution of any driver in regulating VCD. Since the MODIS data are available from 2000 onwards, and the years 2020 and 2021 have the influence of COVID-induced lockdown (Kashyap et al., 2023), the period 2000-2019 is considered in our analysis. Ground-based measurements of productivity might provide better results, but those are not available for the Indian region to assess VCD. ...
Accurate estimation of carbon cycle is a challenging task owing to the complexity and heterogeneity of ecosystems. Carbon Use Efficiency (CUE) is a metric to define the ability of vegetation to sequester carbon from the atmosphere. It is key to understand the carbon sink and source pathways of ecosystems. Here, we quantify CUE using remote sensing measurements to examine its variability, drivers and underlying mechanisms in India for the period 2000-2019, by applying the principal component analyses (PCA), multiple linear regression (MLR) and causal discovery. Our analysis shows that the forests in the hilly regions (HR) and northeast (NE), and croplands in the western areas of South India (SI) exhibit high (>0.6) CUE. The northwest (NW), Indo-Gangetic plain (IGP) and some areas in Central India (CI) show low (<0.3) CUE. In general, the water availability as soil moisture (SM) and precipitation (P) promote higher CUE, but higher temperature (T) and air organic carbon content (AOCC) reduce CUE. It is found that SM has the strongest relative influence (33%) on CUE, followed by P. Also, SM has a direct causal link with all drivers and CUE; reiterating its importance in driving vegetation carbon dynamics (VCD) for the cropland dominated India. The long-term analysis reveals that the low CUE regions in NW (moisture induced greening) and IGP (irrigation induced agricultural boom) have an increasing trend in productivity (greening). However, the high CUE regions in NE (deforestation and extreme events) and SI (warming induced moisture stress) exhibit a decreasing trend in productivity (browning), which is a great concern. Our study, therefore, provides new insights on the rate of carbon allocation and the need of proper planning for maintaining balance in the terrestrial carbon cycle. This is particularly important in the context of drafting policy decisions for the mitigation of climate change, food security and sustainability.
This study examines the impact of PM 2.5 , SO 2 , O 3 , CO, and surface greenness on stroke-related mortality rates in East Africa. Results showed a positive correlation between PM 2.5 and SO 2 , and a negative association between Enhanced Vegetation Index (EVI) and stroke fatality prevalence. The linear regression model showed that the increase of 1 index in EVI could lead to the reduction of stroke-related deaths by 845.57 ± 295.96 deaths per 100,000 persons. Also, a 1 µg/m ³ increase of PM 2.5 and SO 2 concentrations predicted a corresponding increase of stroke-related death by 3.06 ± 1.25 and 139.28 ± 64.33 deaths per 100,000 persons, respectively. Furthermore, the analysis of the influence of these environmental variables on the prevalence of mortality attributable to stroke by age group showed its rise with age, both in intensity and statistical significance. For instance, a rise of 1 unit in EVI predicted the reduction of the stroke-related death rate by 9.18 ± 6.45 and 2133.93 ± 701.07 deaths per 100,000 persons in the age groups of 20–29 and 70–79 years old, respectively. A rise in 1 µg/m ³ of PM 2.5 and SO 2 is expected to trigger the mortality incident rise from 0.05 ± 0.03 to 7.77 ± 3.01 and 4.28 ± 1.40 to 426.21 ± 152.38 deaths per 100,000 persons in respective age groups of 20–29 and 70–79 years. The exposure to CO and O 3 did not demonstrate a significant effect on the stroke-related death rate in the region for the period of the study.
Understanding the factors and mechanisms that influence the impact of urbanization on vegetation growth is crucial for climate change mitigation and urban greening initiatives. However, the long-term evolution mechanisms of these impacts, particularly in the context of changing climate and water scarcity, are not yet fully understood. In this study, we evaluated the long-term indirect impacts of urbanization on vegetation growth across 2,385 county-level cities in China. We found that the trend of vegetation enhancement induced indirectly by urbanization is less pronounced in arid zones compared to humid zones. In addition to the vegetation removal caused by urbanization, the expansion of urban spaces can compensate for vegetation loss by fostering growth, surpassing the effects of economic and population growth. However, in arid zones, the positive impacts and regulation amplitudes of urban spatial expansion on vegetation growth are constrained due to high urban water scarcity. These insights may contribute to a more accurate assessment of carbon gains or losses in urbanized vegetation.
A poluição do ar é problemática presente em muitos contextos socioespaciais afetando milhões de pessoas anualmente no mundo. A intensa urbanização e os riscos socioambientais estão atrelados a mortalidade por doenças cardiorrespiratórias agravadas por contextos atípicos, como o da pandemia da Covid-19. Por meio de dados de mortalidade do Ministério da Saúde e poluentes atmosféricos buscou-se identificar a relação entre a exposição à poluição do ar e os óbitos por Covid -19 na região Norte do Brasil. Utilizamos um modelo estatístico misto binomial negativo com inflação de zero (ZINB) para os óbitos por Covid-19 em nível de município como desfecho, e para a média de longo prazo, poluentes atmosféricos (PM2,5, NO2 e O3). Foram incluídos variáveis-controles - climáticas, demográficas e oferta de equipamentos e profissionais de saúde. Os resultados evidenciam associação positiva entre as mortes por Covid-19 e material particulado (PM2,5) com incremento de cerca de 1,15 µg/m³ no modelo principal, assim como, para os modelos bipoluentes (O3 e NO2) e (PM2,5 e O3). A análise geral confirma o incremento de poluentes atmosféricos no agravamento e consequente mortalidade por Covid-19 para todos os municípios da Região Norte. Contexto atrelado também a baixa escolaridade, a baixa renda, a baixa oferta de leitos e profissionais de saúde, indicando a seletividade espacial existente, principalmente, para populações vulnerabilizadas.
In the spring of 2020, lockdown measures were adopted to control the spread of novel coronavirus and these measurements had large impacts on air quality, regional climate, and vegetation growth. Accurately capturing the response of vegetation to environmental changes is crucial for ecosystem research. However, there might be some divergences in vegetation growth trends from different satellite datasets. To investigate the response of vegetation growth to environmental changes from January to April 2020, we employed two reconstructed Solar-induced chlorophyll fluorescence (SIF) products (CSIF and GOSIF), Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI) and Leaf Area Index (LAI). Results shows that CSIF, GOSIF, NDVI, EVI and LAI captured enhanced vegetation growth during January to March 2020 compared to 2015-2019. However, CSIF and GOSIF show an opposite trend to Vegetation Indices (VIs) and LAI in April. The trends from CSIF and GOSIF also differed with those from TROPOMI SIF and OCO-2 SIF. Biases were attributed to the input MODIS surface reflectance data for reconstructing SIF in the algorithms of GOSIF and CSIF.
Moisture stress has increased substantially across the globe with significant impact on terrestrial ecosystems. Therefore, we examine the sensitivity and response of terrestrial ecosystems to rising moisture stress in India during recent decades (2000 to 2019). The Random Forest approach reveals that soil moisture (SM) is the major driver (31%) of ecosystem functioning and its health in India. Moisture availability in the atmosphere (Vapor Pressure Deficit [VPD], 25.8%) and on land (Climatic Water Deficit [CWD], 24.6%) also have major control on the ecosystem functioning. Terrestrial warming (1.8%) increases evapotranspiration (ET, 10.76%), depletes SM (2.45%), and rises CWD (3.37%) and VPD (1.8%), despite the increase in precipitation (P, 2.54%) in recent decade (2010 to 2019) as compared to previous decade (2000 to 2009). In response to the rising moisture stress, the Water Use Efficiency (WUE) is increasing in the eastern Indo-Gangetic Plain (IGP), Southern India (SI), and eastern Himalaya, but the Carbon Use Efficiency (CUE) is decreasing in these regions. The Resilience method reveals that IGP and Northwest are nonresilient to moisture stress in terms of CUE, whereas SI, the western Central India, eastern Himalaya, and Northeast are nonresilient in terms of WUE. Our results reveal that ecosystem functioning factors (CUE and WUE) are increasing (decreasing) in regions where they are nonresilient (resilient) to moisture stress. Therefore, there is a need of proper ecosystem planning, climate risk management and policies to mitigate the threats of climate change and food security by maintaining ecosystem resilience to moisture stress across the regions.
CONTEXT
About 60% of the world population relies primarily on rice as their staple food, and India ranks second in terms of global rice production. Studies have shown the adverse impact of surface ozone pollution on agriculture, particularly the yield loss (YL) of major staple crops.
OBJECTIVE
(i) To assess the bias associated with ozone data used for YL estimation, (ii) to find the uncertainties in ozone exposure/crop-response methods applied for computing YL and (iii) to analyse the spatio-temporal variability of YL in rice due to surface ozone in India for the period 2005–2020 to assess food security of the country.
METHODS
We use the Tropospheric Emission Spectrometer chemical reanalysis (TCR-2) surface ozone data and the ozone exposure/crop-response functions to compute YL in rice.
RESULTS AND CONCLUSIONS
By using the AOT40 crop-response method, we find a crop production loss (CPL) of about 7.39 million tonnes (Mt) of rice in 2005, which increased to 11.46 Mt. in 2020. The estimated average CPL for the study period is sufficient to feed about 233 million people per year. It also has incurred an economic loss of about $2.92 billion in 2020.
SIGNIFICANCE
Atmospheric pollution must be reduced to protect crop health and ensure food security, as evidenced by the two-fold rise of YL in rice due to ozone pollution during the past decade in India. This is also applicable to all agrarian economies of the world with high atmospheric pollution; reiterating the global significance of this study.
There are substantial changes in global green cover owing to anthropogenic activities and climate change. Here, we estimate the long-term changes in India's green cover, and its contribution from croplands and forests using satellite-based Normalised Difference Vegetation Index (NDVI) and Leaf Area Index (LAI) for the period of 2000–2019. The change in Solar Induced Fluorescence (SiF) is also estimated to understand the variability in the photosynthetic activity and productivity. The increase in NDVI (10%), LAI (11%) and SiF (13%) suggests that India has been greening in the past two decades, which added 996640 km2 of new leaf area during the period. The net vegetated land in India is substantially greening (62.5% area) and marginally browning (14% area). Interestingly, the magnitude of greening in croplands is twice the forests, and is predominant in Zaid (70% area) agricultural season. Therefore, the croplands drive (86.5% contribution) greening of India in the past two decades. The enhanced greening of croplands can be attributed to improved irrigation facilities, as demonstrated by the larger Net Irrigated Area (NIA) and Irrigated Sown Area (ISA). In addition, the effective cropland management, farm mechanisation and use of nitrogen fertilisers are also key to this cropland-based greening in India.
Abstract Plant growth and crop harvest are impacted by both climate change and air pollution. However, their relative importance in crop yields remains elusive, especially in heavily polluted regions. Here we develop crop yield prediction models, based on a large volume of historical crop data, as well as climate and pollution records in China since 1980. A long‐term surface ozone concentration data set is developed from a machine‐learning model and various observations. An assessment of four climate and pollution factors reveals the critical role of particulate and ozone pollution in regulating interannual variations of crop yields in China. During 2010–2018, we find that the particulate pollution mitigation outweighs the negative impacts of concurrent climate change, resulting in 0.5%–1.9% net yield increases nationwide, despite of the ozone increases in the North China Plain. Looking to the future, the impacts of climate change, particularly from surface temperature increase, will dominate over pollution factors and profoundly reduce future maize and rice yields by 0.6 to 2.8% 10 yr−1 by 2050. Our findings call for attention on the threat to future global food security from the absence of pollution mitigation and the persistence of global warming.
India is a country with more than 67% of its population (947 million) residing in rural areas and 33% in urban areas (472 million) as of 2020. Therefore, health of the people living in rural India is very important for its future development plans, economy and growth. Here, we analyse the rural air quality using satellite measurements of NO2 in India, as the sources of NO2 are well connected to the industrial and economic uplift of a nation. Our analyses for the rural regions show distinct seasonal changes with the highest value (2.0 × 1015 molecules per cm2) in winter and the lowest in monsoon (1.5 × 1015 molecules per cm2) seasons. About 41% of the total NO2 pollution in India is from its rural sources, but 59% of the urban sources were focused in the past studies. In addition, around 45% of the rural NO2 pollution is due to road transport, whereas more than 90% of it in urban India comes from the power sector. Our assessment shows that the NO2 exposure in rural regions is as serious as that in urban areas, indicating the need for more effective reduction of population exposure and protection of public health. Henceforth, this study reveals that rural India is gradually getting polluted from its nearby regions as well as from the new sources within. This is a big concern for the public health of the large rural population of India.
India relies heavily on coal-based thermal power plants to meet its energy demands. Sulphur dioxide (SO2) emitted from these plants and industries is a major air pollutant. Analysis of spatial and temporal changes in SO2 using accurate and continuous
observations is required to formulate mitigation strategies to curb the increasing air pollution in India. Here, we present the temporal changes in SO2 concentrations over India in the past four decades (1980–2020). Our analysis shows that the Central
and East India, and Indo-Gangetic Plain (IGP) are the hotspots of SO2, as these regions house a cluster of thermal power plants, petroleum refineries, steel manufacturing units, and cement Industries. Thermal power plants (51%), and manufacturing and construction industries (29%) are the main sources of anthropogenic SO2 in India. Its concentration over India is higher in winter (December–February) and lower in pre-monsoon (March–May) seasons. The temporal analyses reveal that SO2 concentrations in India increased between 1980 and 2010 due to high coal burning and lack of novel technology
to contain the emissions during the period. However, SO2 shows a decreasing trend in recent decade (2010–2020) because of the environmental regulations and implementation of efective control technologies such as the fue gas desulphurisation
(FGD) and scrubber. Since 2010, India's renewable energy production has also been increased substantially when India adopted a sustainable development policy. Therefore, the shift in energy production from conventional coal to renewable sources, solid environmental regulation, better inventory, and effective technology would help to curb SO2 pollution in India. Both economic growth and air pollution control can be performed hand-in-hand by adopting new technology to reduce SO2 and GHG emissions.
The Severe Acute Respiratory Syndrome-COronaVIrus Diseases 2019 (SARS-COVID-19) has sternly affected the entire world in terms of human health, loss of lives, and huge economic losses. However, pandemic-triggered lockdown (LD) events (as a preventive measure) have compelled to stop or reduce major economic activities, exerting positive impacts on the terrestrial environment. We deployed a variety of satellite products (i.e., normalized difference vegetation index (NDVI), solar-induced chlorophyll fluorescence (SIF), and aerosol optical depth (AOD)) along with gridded climatic dataset (temperature (TEMP), precipitation (PREC), and net radiation (NR)) to quantify the changes in vegetation activity (greenness and productivity) during the LD period over the Indian biogeographic provinces (BGPs) as compared to the average conditions over the previous three years (2017-2019). The analysis of the NDVI and SIF data revealed that vegetation greenness and productivity significantly enhanced during LD periods (by up to 37 to 55%, respectively). The influence of climatic drivers (PREC, TEMP, and NR) on vegetation activity was also investigated. We found that the enhancement in the vegetation activity (over BGPs) during the LD period was not entirely driven by the climatic parameters, and was therefore inferred to be also influenced by the LD events. Moreover, vegetation activity around the mining clusters were largely improved during the LD period (by up to 78%) over the coal mining, followed by iron ore mining (up to 63%), and stone mining (up to 41%) clusters) regions. In a nutshell, it can be deliberated that COVID-triggered preventive measures (i.e., country-level LD, travel bans, industry ban, curtail in mining capacity, among others) likely enhanced vegetation health and productivity. Thereby, regulatory measures can be seen as a viable option for improving the terrestrial environmental conditions in the context of climate change in the near future.
Solar radiation is the energy source for crop growth, as well as for the processes of accumulation, distribution, and transfer of photosynthetic products that determine maize yield. Therefore, learning the effects of different solar radiation amounts on maize growth is especially important. The present study focused on the quantitative relationships between solar radiation amounts and dry matter accumulations and transfers in maize. Over two continuous years (2017 and 2018) of field experiments, maize hybrids XY335 and ZD958 were grown at densities of 4.5 × 10⁴ (D1), 7.5 × 10⁴ (D2), 9 × 10⁴ (D3), 10.5 × 10⁴ (D4), and 12 × 10⁴ (D5) plants/ha at Qitai Farm (89°34′E, 44°12′N), Xinjiang, China. Shading levels were 15% (S1), 30% (S2), and 50% (S3) of natural light and no shading (CK). The results showed that the yields of the commonly planted cultivars XY335 and ZD958 at S1, S2, and S3 (increasing shade treatments) were 7.3, 21.2, and 57.6% and 11.7, 31.0, and 61.8% lower than the control yields, respectively. Also, vegetative organ dry matter translocation (DMT) and its contribution to grain increased as shading levels increased under different densities. The dry matter assimilation amount after silking (AADMAS) increased as solar radiation and planting density increased. When solar radiation was <580.9 and 663.6 MJ/m², for XY335 and ZD958, respectively, the increase in the AADMAS was primarily related to solar radiation amounts; and when solar radiation was higher than those amounts for those hybrids, an increase in the AADMAS was primarily related to planting density. Photosynthate accumulation is a key determinant of maize yield, and the contributions of the vegetative organs to the grain did not compensate for the reduced yield caused by insufficient light. Between the two cultivars, XY335 showed a better resistance to weak light than ZD958 did. To help guarantee a high maize yield under weak light conditions, it is imperative to select cultivars that have great stay-green and photosynthetic efficiency characteristics.
The 2019 novel coronavirus pandemic (COVID-19) negatively affected global public health and socioeconomic development. Lockdowns and travel restrictions to contain COVID-19 resulted in reduced human activity and decreased anthropogenic emissions. However, the secondary effects of these restrictions on the biophysical environment are uncertain. Using remotely sensed big data, we investigated how lockdowns and traffic restrictions affected China’s spring vegetation in 2020. Our analyses show that travel decreased by 58% in the first 18 days following implementation of the restrictions across China. Subsequently, atmospheric optical clarity increased and radiation levels on the vegetation canopy were augmented. Furthermore, the spring of 2020 arrived 8.4 days earlier and vegetation 17.45% greener compared to 2015–2019. Reduced human activity resulting from COVID-19 restrictions contributed to a brighter, earlier, and greener 2020 spring season in China. This study shows that short-term changes in human activity can have a relatively rapid ecological impact at the regional scale.
Climate change and air pollution have been a matter of serious concern all over the world in the last few decades. The present review has been carried out in this concern over the Indian cities with significant impacts of both the climate change and air pollution on human health. The expanding urban areas with extreme climate events (high rainfall, extreme temperature, floods, and droughts) are posing human health risks. The intensified heat waves as a result of climate change have led to the elevation in temperature levels causing thermal discomfort and several health issues to urban residents. The study also covers the increasing air pollution levels above the prescribed standards for most of the Indian megacities. The aerosols and PM concentrations have been explored and hazardous health impacts of particles that are inhaled by humans and enter the respiratory system have also been discussed. The air quality during COVID-2019 lockdown in Indian cities with its health impacts has also been reviewed. Finally, the correlation between climate change, air pollution, and urbanizations has been presented as air pollutants (such as aerosols) affect the climate of Earth both directly (by absorption and scattering) and indirectly (by altering the cloud properties and radiation transfer processes). So, the present review will serve as a baseline data for policy makers in analyzing vulnerable regions and implementing mitigation plans for tackling air pollution. The adaptation and mitigation measures can be taken based on the review in Indian cities to reciprocate human health impacts by regular air pollution monitoring and addressing climate change as well.
SARS CoV-2 (COVID-19) coronavirus has been causing enormous suffering, death, and economic losses worldwide. There are rigorous containment measures on industries, non-essential business, transportation, and citizen mobility to check the spread. The lockdowns may have an advantageous impact on reducing the atmospheric pollutants. This study has analyzed the change in atmospheric pollutants, based on the Sentinel–5Ps and ground-station observed data during partial to complete lockdown period in 2020. Results revealed that the mean tropospheric NO2 concentration substantially dropped in 2020 due to lockdown against the same period in 2019 by 18–40% over the major urban areas located in Europe (i.e. Madrid, Milan, Paris) and the USA (i.e. New York, Boston, and Springfield). Conversely, urban areas with partial to no lockdown measures (i.e. Warsaw, Pierre, Bismarck, and Lincoln) exhibited a relatively lower dropdown in mean NO2 concentration (3 to 7.5%). The role of meteorological variability was found to be negligible. Nevertheless, the reduced levels of atmospheric pollutants were primarily attributed to the shutdown of vehicles, power plants, and industrial emissions. Improvement in air quality during COVID-19 may be temporary, but regulatory bodies should learn to reduce air pollution on a long-term basis concerning the trade-offs between the environment, society, and economic growth. The intersection of urban design, health, and environment should be addressed by policy-makers to protect public health and sustainable urban policies could be adopted to build urban resilience against any future emergencies.
COVID-19 has heightened human suffering, undermined the economy, turned the lives of billions of people around the globe upside down, and significantly affected the health, economic, environmental and social domains. This study aims to provide a comprehensive analysis of the impact of the COVID-19 outbreak on the ecological domain, the energy sector, society and the economy and investigate the global preventive measures taken to reduce the transmission of COVID-19. This analysis unpacks the key responses to COVID-19, the efficacy of current initiatives, and summarises the lessons learnt as an update on the information available to authorities, business and industry. This review found that a 72-hour delay in the collection and disposal of waste from infected households and quarantine facilities is crucial to controlling the spread of the virus. Broad sector by sector plans for socioeconomic growth as well as a robust entrepreneurship-friendly economy is needed for the business to be sustainable at the peak of the pandemic. The socioeconomic crisis has reshaped investment in energy and affected the energy sector significantly with most investment activity facing disruption due to mobility restrictions. Delays in energy projects are expected to create uncertainty in the years ahead. This report will benefit governments, leaders, energy firms and customers in addressing a pandemic-like situation in the future.
It is imperative to know the spatial distribution of vegetation trends in India and its responses to both climatic and non-climatic drivers because many ecoregions are vulnerable to global climate change. Here we employed the NDVI3g satellite data over the span of 35 years (1981/82–2015) to estimate vegetation trends and corresponding climatic variables trends (i.e., precipitation, temperature, solar radiation and soil moisture) by using the Mann–Kendall test (τ) and the Theil–Sen median trend. Analysis was performed separately for the two focal periods—(i) the earlier period (1981/82–2000) and (ii) later period (2000–2015)—because many ecoregions experienced more warming after 2000 than the 1980s and 1990s. Our results revealed that a prominent large-scale greening trend (47% of area) of vegetation continued from the earlier period to the later period (80% of area) across the northwestern Plain and Central India. Despite climatologically drier regions, the stronger greening trend was also evident over croplands which was attributed to moisture-induced greening combined with cooling trends of temperature. However, greening trends of vegetation and croplands diminished (i.e., from 84% to 40% of area in kharif season), especially over the southern peninsula, including the west-central area. Such changes were mostly attributed to warming trends and declined soil moisture trends, a phenomenon known as temperature-induced moisture stress. This effect has an adverse impact on vegetation growth in the Himalayas, Northeast India, the Western Ghats and the southern peninsula, which was further exaggerated by human-induced land-use change. Therefore, it can be concluded that vegetation trend analysis from NDVI3g data provides vital information on two mechanisms (i.e., temperature-induced moisture stress and moisture-induced greening) operating in India. In particular, the temperature-induced moisture stress is alarming, and may be exacerbated in the future under accelerated warming as it may have potential implications on forest and agriculture ecosystems, including societal impacts (e.g., food security, employment, wealth). These findings are very valuable to policymakers and climate change awareness-raising campaigns at the national level.
Restrictions to reduce human interaction have helped to avoid greater suffering and death from the COVID-19 pandemic, but have also created socioeconomic hardship. This disruption is unprecedented in the modern era of global observing networks, pervasive sensing and large-scale tracking of human mobility and behaviour, creating a unique test bed for understanding the Earth System. In this Perspective, we hypothesize the immediate and long-term Earth System responses to COVID-19 along two multidisciplinary cascades: energy, emissions, climate and air quality; and poverty, globalization, food and biodiversity. While short-term impacts are dominated by direct effects arising from reduced human activity, longer-lasting impacts are likely to result from cascading effects of the economic recession on global poverty, green investment and human behaviour. These impacts offer the opportunity for novel insight, particularly with the careful deployment of targeted data collection, coordinated model experiments and solution-oriented randomized controlled trials, during and after the pandemic.
The deadly COVID-19 virus has caused a global pandemic health emergency. This COVID-19 has spread its arms to 200 countries globally and the megacities of the world were particularly affected with a large number of infections and deaths, which is still increasing day by day. On the other hand, the outbreak of COVID-19 has greatly impacted the global environment to regain its health. This study takes four megacities (Mumbai, Delhi, Kolkata, and Chennai) of India for a comprehensive assessment of the dynamicity of environmental quality resulting from the COVID-19 induced lockdown situation. An environmental quality index was formulated using remotely sensed biophysical parameters like Particulate Matters PM10 concentration, Land Surface Temperature (LST), Normalized Different Moisture Index (NDMI), Normalized Difference Vegetation Index (NDVI), and Normalized Difference Water Index (NDWI). Fuzzy-AHP, which is a Multi-Criteria Decision-Making process, has been utilized to derive the weight of the indicators and aggregation. The results showing that COVID-19 induced lockdown in the form of restrictions on human and vehicular movements and decreasing economic activities has improved the overall quality of the environment in the selected Indian cities for a short time span. Overall, the results indicate that lockdown is not only capable of controlling COVID-19 spread, but also helpful in minimizing environmental degradation. The findings of this study can be utilized for assessing and analyzing the impacts of COVID-19 induced lockdown situation on the overall environmental quality of other megacities of the world.
India had announced the longest ever lockdown from 25 March 2020 to 14 April 2020 amid COVID-19 pandemic. It was reported that the water quality of the Ganga River has improved as compared to regular during this country-wide lockdown. In the present study, an attempt has been made to study the change in water quality of the river in terms of turbidity purely through remote sensing data, in the absence of ground observations, especially during this time period. The change in spectral reflectance of water along the river in the visible region has been analyzed using the Sentinel-2 multispectral remote sensing data at Haridwar, Kanpur, Prayagraj, and Varanasi stretches of the river. In the present study, it was found that the red and NIR bands are most sensitive, and can be used to estimate the turbidity. Further, the temporal variation in turbidity was also analyzed through normalized difference turbidity index at each location. It was observed that the turbidity in the river has reduced drastically at each stretch of the river. The study elicited that the remote sensing approach can be used to make qualitative estimates on turbidity, even in the absence of field observations.
The Severe Acute Respiratory Syndrome-Coronavirus Disease 2019 (COVID-19) pandemic caused by a novel coronavirus known as SARS-CoV-2 has caused tremendous suffering and huge economic losses. We hypothesized that extreme measures of partial-to-total shutdown might have influenced the quality of the global environment because of decreased emissions of atmospheric pollutants. We tested this hypothesis using satellite imagery, climatic datasets (temperature, and absolute humidity), and COVID-19 cases available in the public domain. While the majority of the cases were recorded from Western countries, where mortality rates were strongly positively correlated with age, the number of cases in tropical regions was relatively lower than European and North American regions, possibly attributed to faster human-to-human transmission. There was a substantial reduction in the level of nitrogen dioxide (NO2: 0.00002 mol m⁻²), a low reduction in CO (<0.03 mol m⁻²), and a low-to-moderate reduction in Aerosol Optical Depth (AOD: ~0.1–0.2) in the major hotspots of COVID-19 outbreak during February–March 2020, which may be attributed to the mass lockdowns. Our study projects an increasing coverage of high COVID-19 hazard at absolute humidity levels ranging from 4 to 9 g m⁻³ across a large part of the globe during April–July 2020 due to a high prospective meteorological suitability for COVID-19 spread. Our findings suggest that there is ample scope for restoring the global environment from the ill-effects of anthropogenic activities through temporary shutdown measures.
Sudden, large-scale, and diffuse human migration can amplify localized outbreaks into widespread epidemics.1–4 Rapid and accurate tracking of aggregate population flows may therefore be epidemiologically informative. Here, we use mobile-phone-data-based counts of 11,478,484 people egressing or transiting through the prefecture of Wuhan between 1 January and 24 January 2020 as they moved to 296 prefectures throughout China. First, we document the efficacy of quarantine in ceasing movement. Second, we show that the distribution of population outflow from Wuhan accurately predicts the relative frequency and geographic distribution of COVID-19 infections through February 19, 2020, across all of China. Third, we develop a spatio-temporal “risk source” model that leverages population flow data (which operationalizes risk emanating from epidemic epicenters) to not only forecast confirmed cases, but also to identify high-transmission-risk locales at an early stage. Fourth, we use this risk source model to statistically derive the geographic spread of COVID-19 and the growth pattern based on the population outflow from Wuhan; the model yields a benchmark trend and an index for assessing COVID-19 community transmission risk over time for different locations. This approach can be used by policy-makers in any nation with available data to make rapid and accurate risk assessments and to plan allocation of limited resources ahead of ongoing outbreaks.
Human life comes to a standstill as many countries shut themselves off from the work due to the novel coronavirus disease pandemic (COVID-19) that hit the world severely in the first quarter of 2020. All types of industries, vehicle movement, and people's activity suddenly halted, perhaps for the first time in modern history. For a long time, it has been stated in various literature that the increased industrialization and anthropogenic activities in the last two decades polluted the atmosphere, hydrosphere, and biosphere. Since the industries and people's activities have been shut off for a month or more in many parts of the world, it is expected to show some improvement in the prevailing conditions in the aforementioned spheres of environment. Here, with the help of remote sensing images, this work quantitatively demonstrated the improvement in surface water quality in terms of suspended particulate matter (SPM) in the Vembanad Lake, the longest freshwater lake in India. The SPM estimated based on established turbidity algorithm from Landsat-8 OLI images showed that the SPM concentration during the lockdown period decreased by 15.9% on average (range: −10.3% to 36.4%, up to 8 mg/l decrease) compared with the pre-lockdown period. Time series analysis of satellite image collections (April 2013 – April 2020) showed that the SPM quantified for April 2020 is the lowest for 11 out of 20 zones of the Vembanad lake. When compared with preceding years, the percentage decrease in SPM for April 2020 is up to 34% from the previous minima.
Containment works
National governments have taken different approaches in response to the coronavirus disease 2019 (COVID-19) pandemic, ranging from draconian quarantines to laissez-faire mitigation strategies. In data from China collected in February 2020, Maier and Brockmann noticed that, unexpectedly, the epidemic did not take off exponentially. Nonexponential spread occurs when the supply of susceptible individuals is depleted on a time scale comparable to the infectious period of the virus. The results of the authors' modeling approach indicate that the public response to the epidemic plus containment policies were becoming effective despite the initial increase in confirmed cases.
Science , this issue p. 742
The outbreak of the 2019 novel coronavirus disease (COVID-19) has caused more than 100,000 people to be infected and has caused thousands of deaths. Currently, the number of infections and deaths is still increasing rapidly. COVID-19 seriously threatens human health, production, life, social functioning and international relations, and has caused widespread concern around the globe. In the fight against COVID-19, geographic information systems (GIS) and big data technologies have played an important role in many aspects, including the rapid aggregation of multisource big data, rapid visualization of epidemic information, spatial tracking of COVID-19, prediction of regional transmission, identification of the spatial allocation of risk and selection of the control level, balance and management of the supply and demand of medical resources, social-emotional guidance and panic elimination, the provision of solid spatial information support for decision-making about COVID-19 prevention and control, measures formulation, and assessment of the effectiveness of COVID-19 prevention and control. GIS has developed and matured relatively quickly and has a complete technological route for data preparation, platform construction, model construction, and map production. However, for the struggle against COVID-19, the main challenge is finding strategies to adjust traditional technical methods and improve speed and accuracy to provide accurate information for rapid social management. Additionally, in the era of big data, data no longer come mainly from the government but are gathered from more diverse enterprises. As a result, the use of GIS faces difficulties in data acquisition and the integration of heterogeneous data, which requires governments, businesses, and academic institutions to jointly promote the formulation of relevant policies. At the technical level, spatial analysis methods for big data are in the ascendancy. Currently and for a long time in the future, the development of GIS should be strengthened to form a data-driven system for rapid knowledge acquisition, which signifies that GIS should be used to reinforce the social operation parameterization of models and methods, especially when providing support for social management.
Undetected cases
The virus causing coronavirus disease 2019 (COVID-19) has now become pandemic. How has it managed to spread from China to all around the world within 3 to 4 months? Li et al. used multiple sources to infer the proportion of early infections that went undetected and their contribution to virus spread. The researchers combined data from Tencent, one of the world's largest social media and technology companies, with a networked dynamic metapopulation model and Bayesian inference to analyze early spread within China. They estimate that ∼86% of cases were undocumented before travel restrictions were put in place. Before travel restriction and personal isolation were implemented, the transmission rate of undocumented infections was a little more than half that of the known cases. However, because of their greater numbers, undocumented infections were the source for ∼80% of the documented cases. Immediately after travel restrictions were imposed, ∼65% of cases were documented. These findings help to explain the lightning-fast spread of this virus around the world.
Science , this issue p. 489
The novel coronavirus outbreak (COVID-19) in mainland China has rapidly spread across the globe. Within 2 mo since the outbreak was first reported on December 31, 2019, a total of 566 Severe Acute Respiratory Syndrome (SARS CoV-2) cases have been confirmed in 26 other countries. Travel restrictions and border control measures have been enforced in China and other countries to limit the spread of the outbreak. We estimate the impact of these control measures and investigate the role of the airport travel network on the global spread of the COVID-19 outbreak. Our results show that the daily risk of exporting at least a single SARS CoV-2 case from mainland China via international travel exceeded 95% on January 13, 2020. We found that 779 cases (95% CI: 632 to 967) would have been exported by February 15, 2020 without any border or travel restrictions and that the travel lockdowns enforced by the Chinese government averted 70.5% (95% CI: 68.8 to 72.0%) of these cases. In addition, during the first three and a half weeks of implementation, the travel restrictions decreased the daily rate of exportation by 81.3% (95% CI: 80.5 to 82.1%), on average. At this early stage of the epidemic, reduction in the rate of exportation could delay the importation of cases into cities unaffected by the COVID-19 outbreak, buying time to coordinate an appropriate public health response.
One of our era's greatest scourges is air pollution, on account not only of its impact on climate change but also its impact on public and individual health due to increasing morbidity and mortality. There are many pollutants that are major factors in disease in humans. Among them, Particulate Matter (PM), particles of variable but very small diameter, penetrate the respiratory system via inhalation, causing respiratory and cardiovascular diseases, reproductive and central nervous system dysfunctions, and cancer. Despite the fact that ozone in the stratosphere plays a protective role against ultraviolet irradiation, it is harmful when in high concentration at ground level, also affecting the respiratory and cardiovascular system. Furthermore, nitrogen oxide, sulfur dioxide, Volatile Organic Compounds (VOCs), dioxins, and polycyclic aromatic hydrocarbons (PAHs) are all considered air pollutants that are harmful to humans. Carbon monoxide can even provoke direct poisoning when breathed in at high levels. Heavy metals such as lead, when absorbed into the human body, can lead to direct poisoning or chronic intoxication, depending on exposure. Diseases occurring from the aforementioned substances include principally respiratory problems such as Chronic Obstructive Pulmonary Disease (COPD), asthma, bronchiolitis, and also lung cancer, cardiovascular events, central nervous system dysfunctions, and cutaneous diseases. Last but not least, climate change resulting from environmental pollution affects the geographical distribution of many infectious diseases, as do natural disasters. The only way to tackle this problem is through public awareness coupled with a multidisciplinary approach by scientific experts; national and international organizations must address the emergence of this threat and propose sustainable solutions.
Accurately quantifying gross primary production (GPP) globally is critical for assessing plant productivity, carbon balance, and carbon-climate feedbacks, while current GPP estimates exhibit substantial uncertainty. Solar-induced chlorophyll fluorescence (SIF) observed by the Orbiting Carbon Observatory-2 (OCO-2) has offered unprecedented opportunities for monitoring land photosynthesis, while its sparse coverage remains a bottleneck for mapping finer-resolution GPP globally. Here, we used the global, OCO-2-based SIF product (GOSIF) and linear relationships between SIF and GPP to map GPP globally at a 0.05° spatial resolution and 8-day time step for the period from 2000 to 2017. To account for the uncertainty of GPP estimates resulting from the SIF-GPP relationship, we used a total of eight SIF-GPP relationships with different forms (universal and biome-specific, with and without intercept) at both site and grid cell levels to estimate GPP. Our results showed that all of the eight SIF-GPP relationships performed well in estimating GPP globally. The ensemble mean 8-day GPP was generally highly correlated with flux tower GPP for 91 eddy covariance flux sites across the globe (R2 = 0.74, Root Mean Square Error = 1.92 g C m−2 d−1). Our fine-resolution GPP estimates showed reasonable spatial and seasonal variations across the globe and fully captured both seasonal cycles and spatial patterns present in our coarse-resolution (1°) GPP estimates based on coarse-resolution SIF data directly aggregated from discrete OCO-2 soundings. SIF-GPP relationships with different forms could lead to significant differences in annual GPP particularly in the tropics. Our ensemble global annual GPP estimate (135.5 ± 8.8 Pg C yr−1) is between the median estimate of non-process based methods and the median estimate of process-based models. Our GPP estimates showed interannual variability in many regions and exhibited increasing trends in many parts of the globe particularly in the Northern Hemisphere. With the availability of high-quality, gridded SIF observations from space (e.g., TROPOMI, FLEX), our novel approach does not rely on any other input data (e.g., climate data, soil properties) and therefore can map GPP solely based on satellite SIF observations and potentially lead to more accurate GPP estimates at regional to global scales. The use of a universal SIF-GPP relationship versus biome-specific relationships can also avoid the uncertainty associated with land cover maps. Our novel, independent GPP product (GOSIF GPP), freely available at our data repository, will be valuable for studying photosynthesis, carbon cycle, agricultural production, and ecosystem responses to climate change and disturbances, informing ecosystem management, and benchmarking terrestrial biosphere and Earth system models.
Global dimming, a decadal decrease in incident global radiation, is often accompanied with an increase in the diffuse‐radiation fraction, and therefore the impact of global dimming on crop production is hard to predict. A popular approach to quantify this impact is statistical analysis of historical climate and crop data, or use of dynamic crop simulation modelling approach. Here we show that statistical analysis of historical data did not provide plausible values for the effect of diffuse radiation versus direct radiation on rice or wheat yield. In contrast, our field experimental study of three years demonstrated a fertilization effect of increased diffuse‐radiation fraction, which partly offset yield losses caused by decreased global radiation, in both crops. The fertilization effect was not attributed to any improved canopy light interception but mainly to the increased radiation use efficiency (RUE). The increased RUE was explained not only by the saturating shape of photosynthetic light‐response curves but also by plant acclimation to dimming that gradually increased leaf nitrogen concentration. Crop harvest index slightly decreased under dimming, thereby discounting the fertilization effect on crop yields. These results challenge existing modelling paradigms, which assume that the fertilization effect on crop yields is mainly attributed to an improved light interception. Further studies on the physiological mechanism of plant acclimation are required to better quantify the global‐dimming impact on agroecosystem productivity under future climate change. This article is protected by copyright. All rights reserved.
Satellite data show increasing leaf area of vegetation due to direct factors (human land-use management) and indirect factors (such as climate change, CO2 fertilization, nitrogen deposition and recovery from natural disturbances). Among these, climate change and CO2 fertilization effects seem to be the dominant drivers. However, recent satellite data (2000–2017) reveal a greening pattern that is strikingly prominent in China and India and overlaps with croplands world-wide. China alone accounts for 25% of the global net increase in leaf area with only 6.6% of global vegetated area. The greening in China is from forests (42%) and croplands (32%), but in India is mostly from croplands (82%) with minor contribution from forests (4.4%). China is engineering ambitious programmes to conserve and expand forests with the goal of mitigating land degradation, air pollution and climate change. Food production in China and India has increased by over 35% since 2000 mostly owing to an increase in harvested area through multiple cropping facilitated by fertilizer use and surface- and/or groundwater irrigation. Our results indicate that the direct factor is a key driver of the ‘Greening Earth’, accounting for over a third, and probably more, of the observed net increase in green leaf area. They highlight the need for a realistic representation of human land-use practices in Earth system models.
In our recent study (Li et al., 2018a), we examined the relationship between solar‐induced chlorophyll fluorescence (SIF) measured from the Orbiting Carbon Observatory‐2 (OCO‐2) and gross primary productivity (GPP) derived from eddy covariance flux towers across the globe, and we discovered that there is a nearly universal relationship between SIF and GPP across a wide variety of biomes. This finding reveals the tremendous potential of SIF for accurately mapping terrestrial photosynthesis globally.
This article is protected by copyright. All rights reserved.
Sowing and harvest dates are a significant source of uncertainty within crop models, especially for regions where high-resolution data are unavailable or, as is the case in future climate runs, where no data are available at all. Global datasets are not always able to distinguish when wheat is grown in tropical and subtropical regions, and they are also often coarse in resolution. South Asia is one such region where large spatial variation means higher-resolution datasets are needed, together with greater clarity for the timing of the main wheat growing season. Agriculture in South Asia is closely associated with the dominating climatological phenomenon, the Asian summer monsoon (ASM). Rice and wheat are two highly important crops for the region, with rice being mainly cultivated in the wet season during the summer monsoon months and wheat during the dry winter. We present a method for estimating the crop sowing and harvest dates for rice and wheat using the ASM onset and retreat. The aim of this method is to provide a more accurate alternative to the global datasets of cropping calendars than is currently available and generate more representative inputs for climate impact assessments.
We first demonstrate that there is skill in the model prediction of monsoon onset and retreat for two downscaled general circulation models (GCMs) by comparing modelled precipitation with observations. We then calculate and apply sowing and harvest rules for rice and wheat for each simulation to climatological estimates of the monsoon onset and retreat for a present day period. We show that this method reproduces the present day sowing and harvest dates for most parts of India. The application of the method to two future simulations demonstrates that the estimated sowing and harvest dates are successfully modified to ensure that the growing season remains consistent with the internal model climate. The study therefore provides a useful way of modelling potential growing season adaptations to changes in future climate.
Solar‐induced chlorophyll fluorescence (SIF) has been increasingly used as a proxy for terrestrial gross primary productivity (GPP). Previous work mainly evaluated the relationship between satellite‐observed SIF and gridded GPP products both based on coarse spatial resolutions. Finer‐resolution SIF (1.3 km × 2.25 km) measured from the Orbiting Carbon Observatory‐2 (OCO‐2) provides the first opportunity to examine the SIF‐GPP relationship at the ecosystem scale using flux tower GPP data. However, it remains unclear how strong the relationship is for each biome and whether a robust, universal relationship exists across a variety of biomes. Here we conducted the first global analysis of the relationship between OCO‐2 SIF and tower GPP for a total of 64 flux sites across the globe encompassing eight major biomes. OCO‐2 SIF showed strong correlations with tower GPP at both mid‐day and daily timescales, with the strongest relationship observed for daily SIF at the 757 nm (R2=0.72, p<0.0001). Strong linear relationships between SIF and GPP were consistently found for all biomes (R2=0.57‐0.79, p<0.0001) except for evergreen broadleaf forests (R2=0.16, p<0.05) at the daily timescale. A higher slope was found for C4 grasslands and croplands than for C3 ecosystems. The generally consistent slope of the relationship among biomes suggests a nearly universal rather than biome‐specific SIF‐GPP relationship, and this finding is an important distinction and simplification compared to previous results. OCO‐2 SIF generally had a better performance for predicting GPP than satellite‐derived vegetation indices and a light use efficiency model. The universal SIF‐GPP relationship can potentially lead to more accurate GPP estimates regionally or globally. Our findings revealed the remarkable ability of finer‐resolution SIF observations from OCO‐2 and other new or future missions (e.g., TROPOMI, FLEX) for estimating terrestrial photosynthesis across a wide variety of biomes and identified their potential and limitations for ecosystem functioning and carbon cycle studies. This article is protected by copyright. All rights reserved.
Atmospheric aerosols influence precipitation by changing the earth’s energy budget and cloud properties. A number of studies have reported correlations between aerosol properties and precipitation data. Despite previous research, it is still hard to quantify the overall effects that aerosols have on precipitation as multiple influencing factors such as relative humidity (RH) can distort the observed relationship between aerosols and precipitation. Thus, in this study, both satellite-retrieved and reanalysis data were used to investigate the relationship between aerosols and precipitation in the Southeast Asia region from 2001 to 2015, with RH considered as a possible influencing factor. Different analyses in the study indicate that a positive correlation was present between Aerosol Optical Depth (AOD) and precipitation over northern Southeast Asia region during the autumn and the winter seasons, while a negative correlation was identified over the Maritime Continent during the autumn season. Subsequently, a partial correlation analysis revealed that while RH influences the long-term negative correlations between AOD and precipitation, it did not significantly affect the positive correlations seen in the winter season. The result of this study provides additional evidence with respect to the critical role of RH as an influencing factor in AOD-precipitation relationship over Southeast Asia.
Signaling through the Ror2 receptor tyrosine kinase promotes invadopodia formation for tumor invasion. Here, we identify intraflagellar transport 20 (IFT20) as a new target of this signaling in tumors that lack primary cilia, and find that IFT20 mediates the ability of Ror2 signaling to induce the invasiveness of these tumors. We also find that IFT20 regulates the nucleation of Golgi-derived microtubules by affecting the GM130-AKAP450 complex, which promotes Golgi ribbon formation in achieving polarized secretion for cell migration and invasion. Furthermore, IFT20 promotes the efficiency of transport through the Golgi complex. These findings shed new insights into how Ror2 signaling promotes tumor invasiveness, and also advance the understanding of how Golgi structure and transport can be regulated.
Carbon cycling in the Amazon is closely linked to atmospheric processes and
climate in the region as a consequence of the strong coupling between the
atmosphere and biosphere. This work examines the effects of changes in net
radiation due to atmospheric aerosol particles and clouds on the net
ecosystem exchange (NEE) of CO2 in the Amazon region. Some of the major
environmental factors affecting the photosynthetic activity of plants, such
as air temperature and relative humidity, were also examined. An algorithm
for clear-sky irradiance was developed and used to determine the relative
irradiance, f, which quantifies the percentage of solar radiation absorbed
and scattered due to atmospheric aerosol particles and clouds. Aerosol
optical depth (AOD) was calculated from irradiances measured with the MODIS
(Moderate Resolution Imaging Spectroradiometer) sensor, onboard the Terra and
Aqua satellites, and was validated with ground-based AOD measurements from
AERONET (Aerosol Robotic Network) sun photometers. Carbon fluxes were
measured using eddy covariance technique at the Large-Scale
Biosphere-Atmosphere Experiment in Amazonia (LBA) flux towers. Two sites were
studied: the Jaru Biological Reserve (RBJ), located in Rondonia, and the
Cuieiras Biological Reserve at the K34 LBA tower (located in a preserved
region in the central Amazon). Analysis was performed continuously from 1999
to 2009 at K34 and from 1999 to 2002 at RBJ, and includes wet, dry and
transition seasons. In the Jaru Biological Reserve, a 29% increase in
carbon uptake (NEE) was observed when the AOD ranged from 0.10 to 1.5 at
550 nm. In the Cuieiras Biological Reserve, the aerosol effect on NEE was
smaller, accounting for an approximate 20% increase in NEE. High aerosol
loading (AOD above 3 at 550 nm) or high cloud cover leads to reductions in
solar flux and strong decreases in photosynthesis up to the point where NEE
approaches zero. The observed increase in NEE is attributed to an enhancement
(~50%) in the diffuse fraction of photosynthetic active radiation
(PAR). The enhancement in diffuse PAR can be done through increases in
aerosols and/or clouds. In the present study, it was not possible to separate
these two components. Significant changes in air temperature and relative
humidity resulting from changes in solar radiation fluxes under high aerosol
loading were also observed at both sites. Considering the long-range
transport of aerosols in the Amazon, the observed changes in NEE for these
two sites may occur over large areas in the Amazon, significantly altering
the carbon balance in the largest rainforest in the world.
Air pollution has become an extremely serious problem. Air pollutants affect both plants and animals. Under polluted conditions, plants develop different physiological, morphological and anatomical changes. Pollutants cause damage to cuticular waxes by which then they enter the leaves through stomata. This further leads to injury to plants which can be either acute or chronic. Changes in stomata due to air pollutants which seem to be small can be of great consequence with respect to survival of the plant during stress. These effects can further lead to disturbing the water balance of leaf or whole plant. Respiration also gets affected because of the exposure of plants to air pollutants. The present paper deals with the effect of air pollutants on stomata as well as on respiration leading to affect gaseous exchange.
Monitoring the main grapevine phenological stages is a key procedure for optimizing vineyard activities and improving yield and quality attributes. Remote sensing may be an effective and practical monitoring tool, as data from on-board satellite sensors can measure vegetative growth. In the current study, a 12-year time series of four main phenophases (budburst, flowering, veraison and harvest) were obtained from an experimental vineyard located in Lisbon (Portugal). LANDSAT surface reflectances were used to calculate the Enhanced Vegetation Index (EVI) and derived metrics. Both time series were then analysed. Results show statistically significant relationships between vegetation metrics and phenological timings and intervals, such as the linkage between the peak greenness and flowering/veraison. The current study highlights the applicability of remote sensing to monitor grapevine phenology in both retrospective and real-time, bringing an added-value to the winemaking sector.
Chlorophyll a fluorescence (ChlF) has been used for decades to study the organization, functioning, and physiology of photosynthesis at the leaf and subcellular levels. ChlF is now measurable from remote sensing platforms. This provides a new optical means to track photosynthesis and gross primary productivity of terrestrial ecosystems. Importantly, the spatiotemporal and methodological context of the new applications is dramatically different compared with most of the available ChlF literature, which raises a number of important considerations. Although we have a good mechanistic understanding of the processes that control the ChlF signal over the short term, the seasonal link between ChlF and photosynthesis remains obscure. Additionally, while the current understanding of in vivo ChlF is based on pulse amplitude-modulated (PAM) measurements, remote sensing applications are based on the measurement of the passive solar-induced chlorophyll fluorescence (SIF), which entails important differences and new challenges that remain to be solved. In this review we introduce and revisit the physical, physiological, and methodological factors that control the leaf-level ChlF signal in the context of the new remote sensing applications. Specifically, we present the basis of photosynthetic acclimation and its optical signals, we introduce the physical and physiological basis of ChlF from the molecular to the leaf level and beyond, and we introduce and compare PAM and SIF methodology. Finally, we evaluate and identify the challenges that still remain to be answered in order to consolidate our mechanistic understanding of the remotely sensed SIF signal.
Significant changes in the photosynthesis of terrestrial vegetation are observed in recent decades due to climate variability, land use and land cover changes, frequent disasters and anthropogenic forcing, but this situation in the Indian context remains largely unexplored. Here, therefore, we estimate the long–term changes in photosynthesis and its key climate drivers in India. The gross photosynthetic activity peaks (FPAR mean = 0.57) in post–monsoon (September, October and November) due to high soil moisture (87.26 kg/m²), good precipitation (5.2 mm/day) and optimum temperature (21.46 °C) conditions, whereas it is smallest (FPAR mean = 0.4) in summer (March, April and May) due to very high temperature (27.37 °C) and lower soil moisture (55.45 kg/m²). The trend in FPAR over 2000–2019 reveals enhanced photosynthetic activity in India. In the northwest, both soil moisture and temperature have a positive influence on greening (i.e. moisture induced greening). Conversely, the unfavourable soil moisture and temperature conditions dominate in the peninsular region to trigger browning there (i.e. warming induced moisture stress). Our analyses suggest that the photosynthetic trend is primarily controlled by trend in the soil moisture (44.31%) and air temperature (32.54%). The warming induced moisture stress can be a serious threat in the future to drive browning in more areas. Extreme events such as droughts, landslides and fires are expected to increase in the hilly regions such as the northeast due to browning. Furthermore, as food production and forest resources influence the economy of a country like India, management and conservation of greenery are inevitable for a sustainable ecosystem.
Human life has come to a standstill when the countrywide lockdown was imposed to curb the infection of the highly contagious COVID-19 pandemic. Mass transportation and industrial activities were prohibited, which has proven advantageous for the environmental quality. This study was conducted to investigate the impact of lockdown on the environmental parameters measured over agricultural ecosystems in the Himalayan foothills [Saharanpur Flux Site (SFS) and Palampur Flux Site (PFS)], India. The UV–Aerosol Index obtained from the Sentinel-5 Precursor satellite falls drastically after imposing lockdown particularly in both the agricultural regions. It reached up to −3.082 and −3.522 during the first lockdown in SFS and PFS, respectively. Furthermore, due to the lesser availability of aerosols into the atmosphere, the availability of direct radiant energy increased, thus led to warming the earth’s surface. Improvement in direct radiant energy and normalized difference vegetation index positively increases the gross primary productivity in SFS and PFS.
The COVID-19 pandemic has caused unprecedent negative impacts on our society, however, evidences show a reduction of anthropogenic pressures on the environment. Due to the high importance of environmental conditions on human life quality, it is crucial to model the impact of COVID-19 lockdown on environmental conditions. Consequently, the objective of this study was to model the impact of COVID-19 lockdown on the urban surface ecological status (USES). To this end, the Landsat-8 images of Milan for three pre-lockdown dates (Feb 13, 2018 (MD1), April 18, 2018 (MD2) and Feb 3, 2020 (MD3)) and one date over the lockdown (April 14, 2020 (MD4)), and Wuhan for three pre-lockdown dates (Dec 17, 2017 (WD1), March 23, 2018 (WD2) and Dec 7, 2019 (WD3)) and one lockdown date (Feb 9, 2020 (WD4)) were used. First, pressure-state-response (PSR) framework parameters including index-based built-up index (IBI), vegetation cover (VC), vegetation health index (VHI), land surface temperature (LST) and Wetness were calculated. Second, by combining the PSR framework parameters based on comprehensive ecological evaluation index (CEEI), the USES were modeled on different dates. Thirdly, the USES during the COVID-19 lockdown was compared with the USES for pre-lockdown. The mean (standard deviation) of CEEI for Milan on MD1, MD2, MD3 and MD4 were 0.52 (0.12), 0.60 (0.19), 0.57 (0.13) and 0.45 (0.16), respectively. Also, these values for Wuhan on WD1, WD2, WD3 and WD4 were 0.63 (0.14), 0.67 (0.15), 0.60 (0.13) and 0.57 (0.13), respectively. Due to the lockdowns, the mean CEEI of built-up, bare soil and green spaces for Milan and Wuhan decreased by [0.18, 0.02, 0.08], [0.13, 0.06, 0.05], respectively. During the lockdown period, the USES improved substantially due to the reduction of anthropogenic activities in the urban environment.
The COVID 19 pandemic led to lockdown and restrictions on anthropogenic activities not only in India but all over the world. This provided an opportunity to study positive effects on environment and subsequent impact on terrestrial ecosystems such as urban, peri-urban, forest and agriculture. A variety of studies presented so far mainly include improved air quality index, water quality, reduced pollutants etc. The present study focused on few novel parameters from both polar and geostationary satellites that are not studied in context to India, and also attempts deriving/quantifying benefits rather than merely indicating qualitative improvements. Due to lack of anthropogenic activities during complete lockdown-1 (21 days from 25 March 2020) in India nighttime cooling of land surface temperature (LST) of the order of 2 to 6 K was observed. Amongst 10 major cities, Bhopal showed highest nighttime cooling. The cooling effect in LST was evident in 80% of industrial units distinctly indicating cooling trend. Vegetation fires were analyzed in 10 fire-prone states of India. Compared to past fouryear average number of occurrences, only 45% fire occurrences occurred during lockdown, indicating strong effect of lockdown. The study also revealed that, there is increase in gross primary production in forest ecosystem to the tune of maximum 38%, during this period. Though delay in rabi crop harvest date by 1 to 2 weeks in majority of north Indian states was observed rise in rabi crop productivity of the order of maximum 34% was observed which is attributed to favorable environmental conditions for net carbon uptake. About 18% reduction in volumetric agricultural water demand was estimated in Indo-Gangetic region, parts of Gujarat and Rajasthan. Apart from controlling the spread of the disease, the lockdown restrictions were thus also able to show positive effects on the environment and ecosystem which might influence to rethink on strategies for sustainable development.
The World Health Organization (WHO) declared the outbreak of the COVID-19 virus as a pandemic on 11 March 2020. As a preventive measure to arrest its spread, the Government of India implemented one of the largest lockdowns in human history on 25 March 2020. This led to closure of a large number of industries and restriction on people movement. Such a measure reduced the concentration of major pollutants in the atmosphere. Present study quantified the impact of change in particulate air pollutants in terms of aerosol optical depth (AOD) on incident light energy over the Indian Sub-continent using satellite imaging observations at 10:30 AM and a radiation transfer algorithm. Change in incident photosynthetically active radiation (IPAR) was used to denote change in level of light energy before and after the commencement of the lockdown. A net increase in IPAR up to 25% was estimated due to lockdown.
Stone quarrying and crushing spits huge stone dust to the environment and causes threats to ecosystem components as well as human health. Imposing emergency lockdown to stop infection of COVID 19 virus on 24.03.2020 in India has created economic crisis but it has facilitated environment to restore its quality. Global scale study has already proved the qualitative improvement of air quality but its possible impact at regional level is not investigated yet. Middle catchment of Dwarka river basin of Eastern India is well known for stone quarrying and crushing and therefore the region is highly polluted. The present study has attempted to explore the impact of forced lockdown on environmental components like Particulate matter (PM) 10, Land surface temperature (LST), river water quality, noise using image and field derived data in pre and during lockdown periods. Result clearly exhibits that Maximum PM10 concentration was 189 to 278 μg/m³ in pre lockdown period and it now ranges from 50 to 60 μg/m³ after 18 days of the commencement of lockdown in selected four stone crushing clusters. LST is reduced by 3–5 °C, noise level is dropped to <65dBA which was above 85dBA in stone crusher dominated areas in pre lockdown period. Adjacent river water is qualitatively improved due to stoppage of dust release to the river. For instance, total dissolve solid (TDS) level in river water adjacent to crushing unit is attenuated by almost two times. When entire world is worried about the appropriate policies for abating environmental pollution, this emergency lockdown shows an absolute way i.e. pollution source management may restore environment and ecosystem with very rapid rate.
A COVID-19 outbreak emerged in Wuhan, China at the end of 2019 and developed into a global pandemic during March 2020. The effects of temperature on the dynamics of the COVID-19 epidemic in China are unknown. Data on COVID-19 daily confirmed cases and daily mean temperatures were collected from 31 provincial-level regions in mainland China between Jan. 20 and Feb. 29, 2020. Locally weighted regression and smoothing scatterplot (LOESS), distributed lag nonlinear models (DLNMs), and random-effects meta-analysis were used to examine the relationship between daily confirmed cases rate of COVID-19 and temperature conditions. The daily number of new cases peaked on Feb. 12, and then decreased. The daily confirmed cases rate of COVID-19 had a biphasic relationship with temperature (with a peak at 10 °C), and the daily incidence of COVID-19 decreased at values below and above these values. The overall epidemic intensity of COVID-19 reduced slightly following days with higher temperatures with a relative risk (RR) was 0.96 (95% CI: 0.93, 0.99). A random-effect meta-analysis including 28 provinces in mainland China, we confirmed the statistically significant association between temperature and RR during the study period (Coefficient = −0.0100, 95% CI: −0.0125, −0.0074). The DLNMs in Hubei Province (outside of Wuhan) and Wuhan showed similar patterns of temperature. Additionally, a modified susceptible-exposed-infectious-recovered (M-SEIR) model, with adjustment for climatic factors, was used to provide a complete characterization of the impact of climate on the dynamics of the COVID-19 epidemic.
The effectiveness and cost are always top factors for policy-makers to decide control measures and most measures had no pre-test before implementation. Due to the COVID-19 pandemic, human activities are largely restricted in many regions in India since mid-March of 2020, and it is a progressing experiment to testify effectiveness of restricted emissions. In this study, concentrations of six criteria pollutants, PM10, PM2.5, CO, NO2, ozone and SO2 during March 16th to April 14th from 2017 to 2020 in 22 cities covering different regions of India were analysed. Overall, around 43, 31, 10, and 18% decreases in PM2.5, PM10, CO, and NO2 in India were observed during lockdown period compared to previous years. While, there were 17% increase in O3 and negligible changes in SO2. The air quality index (AQI) reduced by 44, 33, 29, 15 and 32% in north, south, east, central and western India, respectively. Correlation between cities especially in northern and eastern regions improved in 2020 compared to previous years, indicating more significant regional transport than previous years. The mean excessive risks of PM reduced by ~52% nationwide due to restricted activities in lockdown period. To eliminate the effects of possible favourable meteorology, the WRF-AERMOD model system was also applied in Delhi-NCR with actual meteorology during the lockdown period and an un-favourable event in early November of 2019 and results show that predicted PM2.5 could increase by only 33% in unfavourable meteorology. This study gives confidence to the regulatory bodies that even during unfavourable meteorology, a significant improvement in air quality could be expected if strict execution of air quality control plans is implemented.
During the first 90 days of the COVID-19 outbreak in the United States, over 675,000 confirmed cases of the disease have been announced, posing unprecedented socioeconomic burden to the country. Due to inadequate research on geographic modeling of COVID-19, we investigated county-level variations of disease incidence across the continental United States. We compiled a geodatabase of 35 environmental, socioeconomic, topographic, and demographic variables that could explain the spatial variability of disease incidence. Further, we employed spatial lag and spatial error models to investigate spatial dependence and geographically weighted regression (GWR) and multiscale GWR (MGWR) models to locally examine spatial non-stationarity. The results suggested that even though incorporating spatial autocorrelation could significantly improve the performance of the global ordinary least square model; these models still represent a significantly poor performance compared to the local models. Moreover, MGWR could explain the highest variations (adj. R²: 68.1%) with the lowest AICc compared to the others. Mapping the effects of significant explanatory variables (i.e., income inequality, median household income, the proportion of black females, and the proportion of nurse practitioners) on spatial variability of COVID-19 incidence rates using MGWR could provide useful insights to policymakers for targeted interventions.
The effect of weather on COVID-19 spread is poorly understood. Recently, few studies have claimed that warm weather can possibly slowdown the global pandemic, which has already affected over 1.6 million people worldwide. Clarification of such relationships in the worst affected country, the US, can be immensely beneficial to understand the role of weather in transmission of the disease in the highly populated countries, such as India. We collected the daily data of new cases in 50 US states between Jan 1–Apr 9, 2020 and also the corresponding weather information (i.e., temperature (T) and absolute humidity (AH)). Distribution modeling of new cases across AH and T, helped identify the narrow and vulnerable AH range. We validated the results for 10-day intervals against monthly observations, and also worldwide trends. The results were used to predict Indian regions which would be vulnerable to weather based spread in upcoming months of 2020. COVID-19 spread in the US is significant for states with 4 < AH < 6 g/m3 and number of new cases > 10,000, irrespective of the chosen time intervals for study parameters. These trends are consistent with worldwide observations, but do not correlate well with India so far possibly due the total cases reported per interval < 10,000. The results clarify the relationship between weather parameters and COVID-19 spread. The vulnerable weather parameters will help classify the risky geographic areas in different countries. Specifically, with further reporting of new cases in India, prediction of states with high risk of weather based spread will be apparent.
The spatial and temporal variations of the vegetation carbon flux in China from 1901 to 2005 were studied using the vegetation net primary production (NPP) values from seven Earth-system models. In addition, the temporal and spatial changes in the nitrogen deposition flux in China were studied using the NHx and NOx fluxes from 1901 to 2005. The relationship between changes in the carbon flux, nitrogen flux and climate was analyzed. The results show that (1) over the past 100 years, NPP in China has shown an upward trend. The average trend coefficient is 0.88 and the NPP distribution trend is generally low in the north and high in the south, with a gradual increase from the northwest to the southeast. Temperature, precipitation and radiation are all conducive to plant growth in the direction of the gradient. The correlation coefficients between the ensemble model mean NPP and temperature, precipitation, longwave radiation and shortwave radiation are 0.88,0.73,0.91 and 0.67, respectively. (2) In the past 100 years, the NHx and NOy fluxes in China have shown an upward trend, with trend coefficients of 0.98 and 0.98, respectively, which pass the 99.9% confidence level of the t-test. NHx and NOy fluxes are also generally low in the north and high in the south, with a gradual increase from the northwest to the southeast in a step-like pattern. (3) The spatial distribution of the correlation coefficients between the NHx and NOy fluxes and air temperature is similar, with only slight differences in values. The spatial distribution of the correlation coefficients between the NHx and NOy fluxes and precipitation is similar in overall pattern, but the pattern is relatively complicated, with a positive-negative-positive-negative-positive pattern occurring across the monsoon region from north to south, and a negative-positive-negative-positive pattern occurring beyond the monsoon region from east to west. (4) The spatial distribution of the correlation coefficients between the NHx and NOy fluxes and NPP shows a generally consistent pattern, but the pattern is relatively uneven. The average distribution of the ensemble model mean is positive correlation in northeast China and southwest China, and alternating positive and negative correlation in other regions.
The negative impact of rapid urbanization in developing countries has led to a deterioration of urban and regional air quality. Much attention has been given to the impact of fine particulate pollution on urban public health. However, very little attention has been given to its impact on the regional ecosystem such as the agricultural ecosystem. Thus, we evaluate the direct impact of air pollution on the reduction of wheat photosynthesis by fine particulate matter (PM2.5) pollution in the world’s most heavily polluted area, the North China Plain, using remote sensing observations and ground measurements. We found the following to be true: (1) Heavy PM2.5 pollution could significantly reduce wheat photosynthesis and cause an expositional relationship between the PM2.5 concentration and wheat photosynthesis (R² = 0.9824, P < 0.05); (2) Heavy PM2.5 pollution makes up 2% for the reduction in wheat photosynthesis at all wheat-plant farmlands in the North China Plain, approximately covering an area of 354,400 km²; (3) Increasing heavy PM2.5 pollution significantly reduced wheat photosynthesis by 87% in wheat-planted farmland during 1999–2011. We hope the results presented here could draw attention to the effect of PM2.5 pollution on the agricultural ecosystem and encourage further studies to evaluate the feedback of atmospheric pollution on the agricultural ecosystem using remote sensing.
Abbreviation: Northern China Plain (NCP); normalized difference vegetation index (NDVI); The Moderate Resolution Imaging Spectroradiometer (MODIS); fine particulate matter (PM2.5)
Variations in photosynthesis still cause substantial uncertainties in predicting photosynthetic CO2 uptake rates and monitoring plant stress. Changes in actual photosynthesis that are not related to greenness of vegetation are difficult to measure by reflectance based optical remote sensing techniques. Several activities are underway to evaluate the sun-induced fluorescence signal on the ground and on a coarse spatial scale using space-borne imaging spectrometers. Intermediate-scale observations using airborne-based imaging spectroscopy, which are critical to bridge the existing gap between small-scale field studies and global observations, are still insufficient. Here we present the first validated maps of sun-induced fluorescence in that critical, intermediate spatial resolution, employing the novel airborne imaging spectrometer HyPlant. HyPlant has an unprecedented spectral resolution, which allows for the first time quantifying sun-induced fluorescence fluxes in physical units according to the Fraunhofer Line Depth Principle that exploits solar and atmospheric absorption bands. Maps of sun-induced fluorescence show a large spatial variability between different vegetation types, which complement classical remote sensing approaches. Different crop types largely differ in emitting fluorescence that additionally changes within the seasonal cycle and thus may be related to the seasonal activation and deactivation of the photosynthetic machinery. We argue that sun-induced fluorescence emission is related to two processes: (i) the total absorbed radiation by photosynthetically active chlorophyll and (ii) the functional status of actual photosynthesis and vegetation stress. This article is protected by copyright. All rights reserved.
This article is protected by copyright. All rights reserved.