Timely and accurate extraction of urban land area using the Suomi National Polar-orbiting Partnership Visible Infrared Imaging Radiometer Suite (VIIRS) nighttime light data is important for urban studies. However, a comprehensive assessment of the existing methods for extracting urban land using VIIRS nighttime light data remains inadequate. Therefore, we first reviewed the relevant methods and selected three popular methods for extracting urban land area using nighttime light data. These methods included local-optimized thresholding (LOT), vegetation-adjusted nighttime light urban index (VANUI), integrated nighttime lights, normalized difference vegetation index, and land surface temperature support vector machine classification (INNL-SVM). Then, we assessed the performance of these methods for extracting urban land area based on the VIIRS nighttime light data in seven evaluation areas with various natural and socioeconomic conditions in China. We found that INNL-SVM had the best performance with an average kappa of 0.80, which was 6.67% higher than the LOT and 2.56% higher than the VANUI. The superior performance of INNL-SVM was mainly attributed to the integration of information on nighttime light, vegetation cover, and land surface temperature. This integration effectively reduced the commission and omission errors arising from the overflow effect and low light brightness of the VIIRS nighttime light data. Additionally, INNL-SVM can extract urban land area more easily. Thus, we suggest that INNL-SVM has great potential for effectively extracting urban land with VIIRS nighttime light data at large scales.
Abstract The United Nations’ Sustainable Development Goal (SDG) 3.9 calls for a substantial reduction in deaths attributable to PM 2.5 pollution (DAPP). However, DAPP projections vary greatly and the likelihood of meeting SDG3.9 depends on complex interactions among environmental, socio-economic, and healthcare parameters. We project potential future trends in global DAPP considering the joint effects of each driver (PM 2.5 concentration, death rate of diseases, population size, and age structure) and assess the likelihood of achieving SDG3.9 under the Shared Socioeconomic Pathways (SSPs) as quantified by the Scenario Model Intercomparison Project (ScenarioMIP) framework with simulated PM 2.5 concentrations from 11 models. We find that a substantial reduction in DAPP would not be achieved under all but the most optimistic scenario settings. Even the development aligned with the Sustainability scenario (SSP1-2.6), in which DAPP was reduced by 19%, still falls just short of achieving a substantial (≥20%) reduction by 2030. Meeting SDG3.9 calls for additional efforts in air pollution control and healthcare to more aggressively reduce DAPP.
Estimating the health burden of air pollution against the background of population aging is of great significance for achieving the Sustainable Development Goal 3.9 which aims to substantially reduce the deaths and illnesses from air pollution. Here, we estimated spatiotemporal changes in deaths attributable to PM2.5 air pollution in China from 2000 to 2035 and examined the drivers. The results show that from 2019 to 2035, deaths were projected to decease 15.4% (6.6%-20.7%, 95% CI) and 8.4% (0.6%-13.5%) under the SSP1-2.6 and SSP5-8.5 scenario, respectively, but increase 10.4% (5.1%-20.5%) and 18.1% (13.0%-28.3%) under SSP2-4.5 and SSP3-7.0 scenarios. Population aging will be the leading contributor to increased deaths attributable to PM2.5 air pollution, which will counter the positive gains achieved by improvements in air pollution and healthcare. Region-specific measures are required to mitigate the health burden of air pollution and this requires long-term efforts and mutual cooperation among regions in China.
Abstract Estimating the health burden of air pollution in China against the background of population aging is of great significance for the well-being of elderly individuals and achieving the United Nations Sustainable Development Goals (SDGs). Since previous studies did not fully consider the differences on drivers of deaths attributable to PM 2.5 pollution (DAPP) (i.e., socioeconomic factors, climate factors, and disease mortalities) among provinces in China, the impact of future population aging on DAPP cannot be comprehensively and objectively estimated. In this study, we estimated and verified the spatiotemporal changes of DAPP in China from 2005 to 2017 based on a risk assessment framework. Then, using the latest climate projections, we estimated the changes and drivers in DAPP under four scenarios in China from 2018 to 2035 based on decomposition analysis. The results show that from 2017 to 2035, DAPP in China will decrease between 31.9 (26.6–45.6) thousand and 178.8 (152.5-210.5) thousand across three scenarios (sustainable development (SSP1-2.6), business as usual (SSP2-4.5), and fossil fuel development (SSP5-8.5). We found that population aging will be the leading driver contributing to the increase in DAPP and offset the positive gains achieved by improvement in air pollution and healthcare. In the future, reducing the exposure to air pollution and improving healthcare of the elderly is necessary to mitigate the global health burden of air pollution and this requires mutual cooperation and long-term efforts among countries worldwide.
Abstract Net primary productivity (NPP) is an essential indicator of ecosystem function and sustainability and plays a vital role in the carbon cycle, especially in arid and semiarid grassland ecosystems. Quantifying trends in NPP and identifying the contributing factors are important for understanding the relative impacts of climate change and human activities on grassland degradation. For our case‐study of Kyrgyzstan, we quantified from 2000 to 2014 the spatial and temporal patterns in climate‐driven potential NPP (NPP P ) using the Zhou Guangsheng model specifically developed for Asian grasslands, and actual NPP (NPP A ) using the globally calibrated MOD17A3 NPP data product. By calculating the difference between NPP P and NPP A , we inferred human‐induced NPP (NPP H ) and thereby characterized changes in grassland NPP attributable to anthropogenic activities. The results showed that grassland NPP A in Kyrgyzstan experienced a slight decrease over time at an average rate of −0.87 g C·m −2 ·yr −1 but patterns varied between provinces. Nearly 60% of Kyrgyzstan's grasslands experienced degradation mostly in the northern parts of the country, while grassland NPP A increased over more than 40% of the study area, occurring mostly in the south. Climate change, in particular change in precipitation was the dominant factor driving grassland degradation in the north but human pressures also contributed. In the south, however, human activities were associated with extensive areas of grassland recovery. The results provide important contextual understanding for supporting policy for grassland conservation and restoration under future climate change and intensifying human pressures.
Abstract Air pollution kills nearly 1 million people per year in China. In response, the Chinese government implemented the Air Pollution Prevention and Control Action Plan (APPCAP) from 2013 to 2017 which had a significant impact on reducing PM 2.5 concentration. However, the health benefits of the APPCAP are not well understood. Here we examine the spatiotemporal dynamics of annual deaths attributable to PM 2.5 pollution (DAPP) in China and the contribution from the APPCAP using decomposition analysis. Despite a 36.1% increase in DAPP from 2000 to 2017, The APPCAP-induced improvement in air quality achieved substantial health benefits, with the DAPP in 2017 reduced by 64 thousand (6.8%) compared to 2013. However, the policy is unlikely to result in further major reductions in DAPP and more ambitious policies are required to reduce the health impacts of air pollution by 2030 and meet the United Nation’s Sustainable Development Goal 3.
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