DPIF-Net: a dual path network for rural road extraction based on the fusion of global and local information.

Background: Automatic extraction of roads from remote sensing images can facilitate many practical applications. However, thus far, thousands of kilometers or more of roads worldwide have not been recorded, especially low-grade roads in rural areas. Moreover, rural roads have different shapes and are influenced by complex environments and other interference factors, which has led to a scarcity of dedicated low level category road datasets. Methods: To address these issues, based on convolutional neural networks (CNNs) and tranformers, this article proposes the Dual Path Information Fusion Network (DPIF-Net). In addition, given the severe lack of low-grade road datasets, we constructed the GaoFen-2 (GF-2) rural road dataset to address this challenge, which spans three regions in China and covers an area of over 2,300 km, almost entirely composed of low-grade roads. To comprehensively test the low-grade road extraction performance and generalization ability of the model, comparative experiments are carried out on the DeepGlobe, and Massachusetts regular road datasets. Results: The results show that DPIF-Net achieves the highest IoU and F1 score on three datasets compared with methods such as U-Net, SegNet, DeepLabv3+, and D-LinkNet, with notable performance on the GF-2 dataset, reaching 0.6104 and 0.7608, respectively. Furthermore, multiple validation experiments demonstrate that DPIF-Net effectively preserves improved connectivity in low-grade road extraction with a modest parameter count of 63.9 MB. The constructed low-grade road dataset and proposed methods will facilitate further research on rural roads, which holds promise for assisting governmental authorities in making informed decisions and strategies to enhance rural road infrastructure.

Spatiotemporally continuous PM2.5 dataset in the Mekong River Basin from 2015 to 2022 using a stacking model.

With the potential to cause millions of deaths, PM2.5 pollution has become a global concern. In Southeast Asia, the Mekong River Basin (MRB) is experiencing heavy PM2.5 pollution and the existing PM2.5 studies in the MRB are limited in terms of accuracy and spatiotemporal coverage. To achieve high-accuracy and long-term PM2.5 monitoring of the MRB, fused aerosol optical depth (AOD) data and multi-source auxiliary data are fed into a stacking model to estimate PM2.5 concentrations. The proposed stacking model takes advantage of convolutional neural network (CNN) and Light Gradient Boosting Machine (LightGBM) models and can well represent the spatiotemporal heterogeneity of the PM2.5-AOD relationship. In the cross-validation (CV), comparison with CNN and LightGBM models shows that the stacking model can better suppress overfitting, with a higher coefficient of determination (R2) of 0.92, a lower root mean square error (RMSE) of 5.58 µg/m3, and a lower mean absolute error (MAE) of 3.44 µg/m3. For the first time, the high-accuracy PM2.5 dataset reveals spatially and temporally continuous PM2.5 pollution and variations in the MRB from 2015 to 2022. Moreover, the spatiotemporal variations of annual and monthly PM2.5 pollution are also investigated at the regional and national scales. The dataset will contribute to the analysis of the causes of PM2.5 pollution and the development of mitigation policies in the MRB.

A Joint Encryption and Compression Algorithm for Multiband Remote Sensing Image Transmission.

Due to the increasing capabilities of cybercriminals and the vast quantity of sensitive data, it is necessary to protect remote sensing images during data transmission with "Belt and Road" countries. Joint image compression and encryption techniques exhibit reliability and cost-effectiveness for data transmission. However, the existing methods for multiband remote sensing images have limitations, such as extensive preprocessing times, incompatibility with multiple bands, and insufficient security. To address the aforementioned issues, we propose a joint encryption and compression algorithm (JECA) for multiband remote sensing images, including a preprocessing encryption stage, crypto-compression stage, and decoding stage. In the first stage, multiple bands from an input image can be spliced together in order from left to right to generate a grayscale image, which is then scrambled at the block level by a chaotic system. In the second stage, we encrypt the DC coefficient and AC coefficient. In the final stage, we first decrypt the DC coefficient and AC coefficient, and then restore the out-of-order block through the chaotic system to get the correct grayscale image. Finally, we postprocess the grayscale image and reconstruct it into a remote sensing image. The experimental results show that JECA can reduce the preprocessing time of the sender by 50% compared to existing joint encryption and compression methods. It is also compatible with multiband remote sensing images. Furthermore, JECA improves security while maintaining the same compression ratio as existing methods, especially in terms of visual security and key sensitivity.

Spatio-Temporal Changes in Ecosystem Quality across the Belt and Road Region.

The Silk Road Economic Belt and the 21st Century Maritime Silk Road Initiative (BRI) proposed in 2013 by China has greatly accelerated the social and economic development of the countries along the Belt and Road (B&R) region. However, the international community has questioned its impact on the ecological environment and a comprehensive assessment of ecosystem quality changes is lacking. Therefore, this study proposes an objective and automatic method to assess ecosystem quality and analyzes the spatiotemporal changes in the B&R region. First, an ecosystem quality index (EQI) is established by integrating the vegetation status derived from three remote sensing ecological parameters including the leaf area index, fractional vegetation cover and gross primary productivity. Then, the EQI values are automatically categorized into five ecosystem quality levels including excellent, good, moderate, low and poor to illustrate their spatiotemporal changes from the years 2016 to 2020. The results indicate that the spatial distributions of the EQIs across the B&R region exhibited similar patterns in the years 2016 and 2020. The regions with excellent levels accounted for the lowest proportion of less than 12%, while regions with moderate, low and poor levels accounted for more than 68% of the study area. Moreover, based on the EQI pattern analysis between the years 2016 and 2020, the regions with no significant EQI change accounted for up to 99.33% and approximately 0.45% experienced a significantly decreased EQI. Therefore, this study indicates that the ecosystem quality of the B&R region was relatively poor and experienced no significant change in the five years after the implementation of the "Vision and Action to Promote the Joint Construction of the Silk Road Economic Belt and the 21st Century Maritime Silk Road". This study can provide useful information for decision support on the future ecological environment management and sustainable development of the B&R region.

Remote Sensing Image Scene Classification in Hybrid Classical-Quantum Transferring CNN with Small Samples.

The scope of this research lies in the combination of pre-trained Convolutional Neural Networks (CNNs) and Quantum Convolutional Neural Networks (QCNN) in application to Remote Sensing Image Scene Classification(RSISC). Deep learning (RL) is improving by leaps and bounds pretrained CNNs in Remote Sensing Image (RSI) analysis, and pre-trained CNNs have shown remarkable performance in remote sensing image scene classification (RSISC). Nonetheless, CNNs training require massive, annotated data as samples. When labeled samples are not sufficient, the most common solution is using pre-trained CNNs with a great deal of natural image datasets (e.g., ImageNet). However, these pre-trained CNNs require a large quantity of labelled data for training, which is often not feasible in RSISC, especially when the target RSIs have different imaging mechanisms from RGB natural images. In this paper, we proposed an improved hybrid classical-quantum transfer learning CNNs composed of classical and quantum elements to classify open-source RSI dataset. The classical part of the model is made up of a ResNet network which extracts useful features from RSI datasets. To further refine the network performance, a tensor quantum circuit is subsequently employed by tuning parameters on near-term quantum processors. We tested our models on the open-source RSI dataset. In our comparative study, we have concluded that the hybrid classical-quantum transferring CNN has achieved better performance than other pre-trained CNNs based RSISC methods with small training samples. Moreover, it has been proven that the proposed algorithm improves the classification accuracy while greatly decreasing the amount of model parameters and the sum of training data.

Chinese Soil Moisture Observation Network and Time Series Data Set for High Resolution Satellite Applications.

High-quality ground observation networks are an important basis for scientific research. Here, an automatic soil observation network for high-resolution satellite applications in China (SONTE-China) was established to measure both pixel- and multilayer-based soil moisture and temperature. SONTE-China is distributed across 17 field observation stations with a variety of ecosystems, covering both dry and wet zones. In this paper, the average root mean squared error (RMSE) of station-based soil moisture for well-characterized SONTE-China sites is 0.027 m3/m3 (0.014~0.057 m3/m3) following calibration for specific soil properties. The temporal and spatial characteristics of the observed soil moisture and temperature in SONTE-China conform to the geographical location, seasonality and rainfall of each station. The time series Sentinel-1 C-band radar signal and soil moisture show strong correlations, and the RMSE of the estimated soil moisture from radar data was lower than 0.05 m3/m3 for the Guyuan and Minqin stations. SONTE-China is a soil moisture retrieval algorithm that can validate soil moisture products and provide basic data for weather forecasting, flood forecasting, agricultural drought monitoring and water resource management.

Influences of environmental factors on the dissipation of green tides in the Yellow Sea, China.

Green tides attack the Yellow Sea every year since 2007 and have caused substantial financial loss. Based on Haiyang-1C/Coastal zone imager (HY-1C/CZI) and Terra/MODIS satellite images, the temporal and spatial distribution of green tides floating in the Yellow Sea during 2019 was extracted. The relationships between the growth rate of the green tides and the environmental factors including sea surface temperature (SST), photosynthetically active radiation (PAR), sea surface salinity (SSS), nitrate and phosphate during the green tides' dissipation phase has been detected. Based on the maximum likelihood estimation, a regression model that includes SST, PAR and phosphate was recommended to predict the growth rate of the green tides in the dissipation phase (R2 = 0.63), and this model was also examined using Bayesian information criterion and Akaike information criterion. When the average SST in the study area was above 23.6 °C, the coverage of green tides began to decrease with the increase in temperature under the influence of PAR. The growth rate of the green tides was related to SST (R = -0.38), PAR (R = -0.67) and phosphate (R = 0.40) in the dissipation phase. Compared with HY-1C/CZI, the green tide area extracted using Terra/MODIS tended to be underestimated when the green tide patches were smaller than 11.2 km2. Otherwise, the lower spatial resolution of MODIS resulted in larger mixed pixels of water and algae, which would overestimate the total area of the green tides.

An Improved Spatiotemporal Data Fusion Method for Snow-Covered Mountain Areas Using Snow Index and Elevation Information.

Remote sensing images with high spatial and temporal resolution in snow-covered areas are important for forecasting avalanches and studying the local weather. However, it is difficult to obtain images with high spatial and temporal resolution by a single sensor due to the limitations of technology and atmospheric conditions. The enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM) can fill in the time-series gap of remote sensing images, and it is widely used in spatiotemporal fusion. However, this method cannot accurately predict the change when there is a change in surface types. For example, a snow-covered surface will be revealed as the snow melts, or the surface will be covered with snow as snow falls. These sudden changes in surface type may not be predicted by this method. Thus, this study develops an improved spatiotemporal method ESTARFM (iESTARFM) for the snow-covered mountain areas in Nepal by introducing NDSI and DEM information to simulate the snow-covered change to improve the accuracy of selecting similar pixels. Firstly, the change in snow cover is simulated according to NDSI and DEM. Then, similar pixels are selected according to the change in snow cover. Finally, NDSI is added to calculate the weights to predict the pixels at the target time. Experimental results show that iESTARFM can reduce the bright abnormal patches in the land area compared to ESTARFM. For spectral accuracy, iESTARFM performs better than ESTARFM with the root mean square error (RMSE) being reduced by 0.017, the correlation coefficient (r) being increased by 0.013, and the Structural Similarity Index Measure (SSIM) being increased by 0.013. For spatial accuracy, iESTARFM can generate clearer textures, with Robert's edge (Edge) being reduced by 0.026. These results indicate that iESTARFM can obtain higher prediction results and maintain more spatial details, which can be used to generate dense time series images for snow-covered mountain areas.

QUantitative and Automatic Atmospheric Correction (QUAAC): Application and Validation.

The difficulty of atmospheric correction based on a radiative transfer model lies in the acquisition of synchronized atmospheric parameters, especially the aerosol optical depth (AOD). At the moment, there is no fully automatic and high-efficiency atmospheric correction method to make full use of the advantages of geostationary meteorological satellites in large-scale and efficient atmospheric monitoring. Therefore, a QUantitative and Automatic Atmospheric Correction (QUAAC) method is proposed which can efficiently correct high-spatial-resolution (HSR) satellite images. QUAAC uses the atmospheric aerosol products of geostationary satellites to match the synchronized AOD according to the temporal and spatial information of HSR satellite images. This method solves the problem that the AOD is difficult to obtain or the accuracy is not high enough to meet the demand of atmospheric correction. By using the obtained atmospheric parameters, atmospheric correction is performed to obtain the surface reflectance (SR). The whole process can achieve fully automatic operation without manual intervention. After QUAAC applied to Gaofen-2 (GF-2) HSR satellite and Himawari-8 (H-8) geostationary satellite, the results show that the effect of QUAAC correction is slightly better than that of the Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes (FLAASH) correction, and the QUAAC-corrected surface spectral curves have good coherence to that of the synchronously measured by field experiments.

Soil Moisture Content Retrieval from Remote Sensing Data by Artificial Neural Network Based on Sample Optimization.

Soil moisture content (SMC) plays an essential role in geoscience research. The SMC can be retrieved using an artificial neural network (ANN) based on remote sensing data. The quantity and quality of samples for ANN training and testing are two critical factors that affect the SMC retrieving results. This study focused on sample optimization in both quantity and quality. On the one hand, a sparse sample exploitation (SSE) method was developed to solve the problem of sample scarcity, resultant from cloud obstruction in optical images and the malfunction of in situ SMC-measuring instruments. With this method, data typically excluded in conventional approaches can be adequately employed. On the other hand, apart from the basic input parameters commonly discussed in previous studies, a couple of new parameters were optimized to improve the feature description. The Sentinel-1 SAR and Landsat-8 images were adopted to retrieve SMC in the study area in eastern Austria. By the SSE method, the number of available samples increased from 264 to 635 for ANN training and testing, and the retrieval accuracy could be markedly improved. Furthermore, the optimized parameters also improve the inversion effect, and the elevation was the most influential input parameter.

Probing the dynamic characteristics of aerosol originated from South Asia biomass burning using POLDER/GRASP satellite data with relevant accessory technique design.

The dynamic characteristics of biomass burning aerosol originated from South Asia are investigated in this research using nearly 9 years of POLDER/GRASP satellite aerosol dataset. The POLDER/GRASP remote sensing data can provide global, repeatable, various, and sufficient real-world aerosol information even in the remote ocean region, which can't be offered by the ground measurement, laboratory observation or model simulation. The MODIS thermal anomalies/fire dataset and HYSPLIT backward trajectory are applied to search the aerosol originated from South Asia biomass burning. The biomass burning aerosol originated from South Asia could transport to and influence the north part of Indian Ocean (including Bay of Bengal and Arabian Sea), the north part of Indo-China Peninsula, South China, and even far to the Pacific Ocean (including part of East China Sea and South China Sea). The chemical, physical and optical characteristics of biomass burning aerosol over land and over ocean show different features and evolution patterns. Such difference is caused by the different ambient environment and different mixed aerosol during the transport process (urban/industrial aerosol over land and sea salt over ocean). During the 48-hours aging process, the volume fraction of black carbon, AAOD and Angstrom Exponent decrease. Meanwhile, the aerosol sphere fraction and SSA increase. The biomass burning aerosol over land shows a more obvious evolution trend than that over ocean. The biomass burning aerosol over ocean generally have higher SSA and lower volume fraction of black carbon, aerosol sphere fraction, AAOD and Angstrom Exponent. The aerosol radiative forcing efficiency also varies between land and ocean, due to their different features of aerosol and surface properties. In general, a negative clear-sky aerosol radiative forcing efficiency (cooling effect) at the TOA is observed. The aerosol cooling effect at the TOA over ocean (-82 W/m2 on average) is much stronger than that over land (-36 W/m2 on average). During the 48-hours aging process, a significant enhancement of the negative radiative forcing efficiency at the TOA is found over land. Over ocean, the enhancement of the negative radiative forcing efficiency at the TOA is weaker.

Remote sensing algorithms for estimation of fractional vegetation cover using pure vegetation index values: A review.

Green fractional vegetation cover (fc ) is an important phenotypic factor in the fields of agriculture, forestry, and ecology. Spatially explicit monitoring of fc via relative vegetation abundance (RA) algorithms, especially those based on scaled maximum/minimum vegetation index (VI) values, has been widely investigated in remote sensing research. Although many studies have explored the effectiveness of RA algorithms over the past 30 years, a literature review summarizing the corresponding theoretical background, issues, current state-of-the-art techniques, challenges, and prospects has not yet been published. The overall objective of the present study was to accomplish a comprehensive and systematic review of RA algorithms considering these factors based on the scientific papers published from January 1990 to November 2019. This review revealed that the key issues related to RA algorithms is the determination of the appropriate normalized difference vegetation index (NDVI) values of the full vegetation cover and bare soil (denoted hereafter by NDVI∞ and NDVIS, respectively). The existing methods used to correct for these issues were investigated, and their advantages and disadvantages are discussed in depth. In literature trends, we found that the number of reported studies in which RA algorithms were used has increased consistently over time, and that most authors tend to utilize the linear NDVI model, rather than other models in the RA algorithm family. We also found that RA algorithms have been utilized to analyze the images with spatial resolutions ranging from the sub-meter to kilometer, most commonly, using images of 30-m spatial resolution. Finally, current challenges and forward-looking insights in remote estimation of fc using RA algorithms are discussed to guide future research and directions.

Spatial and temporal variations of air quality and six air pollutants in China during 2015-2017.

Air pollution has aroused significant public concern in China, therefore, long-term air-quality data with high temporal and spatial resolution are needed to understand the variations of air pollution in China. However, the yearly variations with high spatial resolution of air quality and six air pollutants are still unknown for China until now. Therefore, in this paper, we analyze the spatial and temporal variations of air quality and six air pollutants in 366 cities across mainland China during 2015-2017 for the first time to the best of our knowledge. The results indicate that the annual mean mass concentrations of PM2.5, PM10, SO2, and CO all decreased year by year during 2015-2017. However, the annual mean NO2 concentrations were almost unchanged, while the annual mean O3 concentrations increased year by year. Anthropogenic factors were mainly responsible for the variations of air quality. Further analysis suggested that PM2.5 and PM10 were the main factors influencing air quality, while NO2 played an important role in the formation of PM2.5 and O3. These findings can provide a theoretical basis for the formulation of future air-pollution control policy in China.

Author Correction: A lake data set for the Tibetan Plateau from the 1960s, 2005, and 2014.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Biomass burning aerosol characteristics for different vegetation types in different aging periods.

Eighteen years of sun/sky photometer measurements at seven worldwide AErosol RObotic NETwork (AERONET) sites in typical biomass burning regions were used in this research. The AERONET measurements were analyzed with the help of Moderate-resolution Imaging Spectroradiometer (MODIS) fire products and the HYbrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model. The variation in the physicochemical and optical properties of biomass burning aerosols (BBAs), as well as their shortwave radiative forcing, was revealed for different vegetation types in different aging periods. The result indicated that, with aerosol aging, the BBA characteristics have a non-negligible evolution trend with obvious clustering features for different burning vegetation types. During the aging process, the volume fraction of black carbon (BC) declined (with a maximum drop of 38%) accompanied by particle size growth (with a maximum increment of 0.017 µm). Driven by the change in physicochemical properties, the Single Scattering Albedo (SSA) and the asymmetry factor increased as the aerosol aged (with maximum increments of 0.026 and 0.018 for the SSA and asymmetry factor respectively). The grass and shrub type had a higher volume fraction of BC (2.5 times higher than that in the forest and peat type) and a smaller fine mode volume median radius (with a difference of 0.037 µm from that of the forest and peat type). Such a phenomenon results in a lower SSA (with a difference of 0.103) and asymmetry factor (with a difference of 0.035) in the grass and shrub type when compared to the forest and peat type. Negative (-74 to -30 W/m2) clear-sky top of atmosphere (TOA) shortwave radiative forcing, strengthened during the aging process, was generally found for BBA. The BBA in the forest and peat region usually had stronger negative radiative forcing efficiency.

High-resolution inventory of mercury emissions from biomass burning in tropical continents during 2001-2017.

Mercury emissions from biomass burning contribute significantly to the atmospheric mercury budget and the interannual variation of mercury concentrations in the troposphere. This study developed a high-resolution (0.1°â€¯× 0.1°) monthly inventory of mercury emissions from biomass burning across five land types in the tropical continents (Central and South America, Africa, and South and Southeast Asia) during 2001-2017. The inventory estimates of mercury emissions from biomass burning are based on the newly released MCD64A1 Version 6 Burned Area data product, satellite and observational data of biomass density, and spatial and temporal variable combustion factors. Results from the inventory demonstrated that during 2001-2017, the average annual mercury emissions from biomass burning in tropical continents was 497 Mg and ranged from 289 Mg to 681 Mg. Forest fires were the largest contributor, accounting for 61% (300 Mg) of the total mercury emissions from biomass burning, followed by fires in woody savanna/shrubland (30%, 151 Mg), savanna/grassland (7%, 35 Mg), peatland (1%, 6 Mg), and cropland (1%, 5 Mg). However, these proportions varied between the continents; in the Americas and Asia, the largest biomass burning emissions came from forest fires, and in Africa the largest emissions were from fires woody savanna/shrubland. Between the three continents, Africa released 41% of the mercury emissions from biomass burning (202 Mg year-1), Asia released 31% (154 Mg year-1), and the Americas released 28% (141 Mg year-1). The total mercury emissions from biomass burning in these tropical continents exhibited strong interannual variations from 2001 to 2017, with peak emissions in March and August to September, and forest fires were the primary land type controlling the interannual variations.

Contribution of meteorological factors to particulate pollution during winters in Beijing.

Associated with its modernization, Beijing has experienced significant fine particulate matter (PM2.5) pollution, especially in winter. In 2016, severe PM2.5 pollution (PM2.5 > 250 µg/m3) lasted over 6 days and affected over 23 million people. A major challenge in dealing with this issue is the uncertainty regarding the influence of individual meteorological factors to the overall PM2.5 concentration in Beijing. Thus, applying an empirical regression method to long-term ground-based PM2.5 data and meteorological sounding measurements, we attempted to analyze the influence of individual meteorological factors on PM2.5 pollution during winters in Beijing. We found that horizontal dilution and vertical aggregation plays a major role in PM2.5 pollution during the winter of 2016. The impact of horizontal wind on PM2.5 concentration in Beijing was mainly from its dilution, the dilution of northerly wind contributed 27.8% in 2016, far below its contribution in 2015 (32.2%). The contribution from the growing vertical aggregation observed in 2016 was mainly the result of both the lower height of the planetary boundary layer and the greater depth of the temperature inversion. The dilution of the planetary boundary layer height contributed 9.8% to PM2.5 pollution in 2016, 5.4% lower than that in 2017. Compared with the temperature difference of the inversion layer, the temperature inversion depth better reflects the aggregated impact of temperature inversions to PM2.5, which was 10.9% in 2015, and the ratio rose to 14.3% in 2016. Relative humidity is also an important impacting factor, which contributed 41.0%, far higher than the ratio in 2017 (26.7%). Such results imply that we should focus on not only local emission control, but also horizontal atmospheric transport and meteorological conditions in order to provide a more accurate analysis of pollution mechanisms, conductive to air pollution governance in Beijing.

Underlying causes of PM2.5-induced premature mortality and potential health benefits of air pollution control in South and Southeast Asia from 1999 to 2014.

Quantification of spatial and temporal variations in premature mortality attributable to PM2.5 has important implications for air quality control in South and Southeast Asia (SSEA). The number of PM2.5-induced premature deaths during 1999-2014 in SSEA was estimated using an integrated exposure-response model based on 0.01°â€¯× 0.01° satellite-retrieved PM2.5 data, population density, and spatially and temporally variable baseline mortality data. The results showed extremely high premature death rates in North India and Bangladesh. PM2.5-induced premature deaths in SSEA increased with small interannual variations from 1999 to 2014 owing to the interannual variations in PM2.5 concentrations. Moreover, four scenarios on the effects of premature deaths by PM2.5 mitigation efforts based on World Health Organization (WHO) air quality guidelines (AQG) and interim targets (ITs) were investigated for each disease and each country during 1999-2014. Four scenarios based on WHO AQG (10 µg/m3), IT-3 (15 µg/m3), IT-2 (25 µg/m3), and IT-1 (35 µg/m3) resulted in 69.3%, 49.1%, 25.4%, and 12.8% reductions compared to the total reference premature deaths (1256,300), which was calculated using the original PM2.5 datasets. Overall, stroke was the most serious disease associated with air pollution, causing 40% of total premature deaths. Ischemic heart disease was the largest contributor (58%) to the deaths in relatively cleaner air (Scenario 1). The annual rate of change in premature deaths in South Asian countries (India, Bangladesh, and Pakistan) was higher than that in Southeast Asian countries under all scenarios. The results for different scenarios provide insight into the largest health benefits of PM2.5 reduction efforts.

Long-term trends and spatial patterns of PM2.5-induced premature mortality in South and Southeast Asia from 1999 to 2014.

Fine particulate matter (PM2.5) poses a potential threat to human health, including premature mortality under long-term exposure. Based on a long-term series of high-resolution (0.01°×0.01°) satellite-retrieved PM2.5 concentrations, this study estimated the premature mortality attributable to PM2.5 in South and Southeast Asia (SSEA) from 1999 to 2014. Then, the long-term trends and spatial characteristics of PM2.5-induced premature deaths (1999-2014) were analyzed using trend analyses and standard deviation ellipses. Results showed the estimated number of PM2.5-induced average annual premature deaths in SSEA was 1,447,000. The numbers increased from 1,179,400 in 1999 to 1,724,900 in 2014, with a growth rate of 38% and net increase of 545,500. Stroke and ischemic heart disease were the two principal contributors, accounting for 39% and 35% of the total, respectively. High values were concentrated in North India, Bangladesh, East Pakistan, and some metropolitan areas of Southeast Asia. An estimated 991,600 deaths in India was quantified (i.e., ~69% of the total premature deaths in SSEA). The long-term trends (1999-2014) of PM2.5-related premature mortality exhibited consistent incremental tendencies in all countries except Sri Lanka. The findings of this study suggest that strict controls of PM2.5 concentrations in SSEA are urgently required.