Previous studies have reported that atmospheric elemental carbon (EC) may pose potentially elevated toxicity when compared to total ambient fine particulate matter (PM2.5). However, most research on EC has been conducted in the US and Europe, whereas China experiences significantly higher EC pollution levels. Investigating the health impact of EC exposure in China presents considerable challenges due to the absence of a monitoring network to document long-term EC levels. Despite extensive studies on total PM2.5 in China over the past decade and a significant decrease in its concentration, changes in EC levels and the associated mortality burden remain largely unknown. In our study, we employed a combination of satellite remote sensing, available ground observations, machine learning techniques, and atmospheric big data to predict ground EC concentrations across China for the period 2005–2018, achieving a spatial resolution of 10 km. Our findings reveal that the national average annual mean EC concentration has remained relatively stable since 2005, even as total PM2.5 levels have substantially decreased. Furthermore, we calculated the all-cause non-accidental deaths attributed to long-term EC exposure in China using baseline mortality data and pooled mortality risk from a cohort study. This analysis unveiled significant regional disparities in the mortality burden resulting from long-term EC exposure in China. These variations can be attributed to varying levels of effectiveness in EC regulations across different regions. Specifically, our study highlights that these regulations have been effective in mitigating EC-related health risks in first-tier cities. However, in regions characterized by a highconcentration of coal-power plants and industrial facilities, additional efforts are necessary to control emissions. This observation underscores the importance of tailoring environmental policies and interventions to address the specific challenges posed by varying emission sources and regional contexts.
Focus on Atmospheric Remote Sensing and Environmental Change
Guest Editors
- Zhengqiang Li, Aerospace Information Research Institute, Chinese Academy of Sciences, China
- Jason Blake Cohen, China University of Mining and Technology, China
- Kai Qin, China University of Mining and Technology, China
- Jintai Lin, Peking University, China
- Zhe Jiang, University of Science and Technology of China, China
- Xiaomeng Jin, Rutgers University, USA
Scope
Atmospheric remote sensing provides a basis upon which quantification of the atmosphere and its changes over time can be clearly made. By being able to quantify the loadings, spatial, and temporal distribution of various gasses, aerosols, hydrometeors, and dust, more clear information and a reduction of uncertainty about the actual state of the atmosphere is possible. This in turn can have direct effects on the global climate system, air pollution, and the larger atmospheric environment. Changes in greenhouse gasses, radiative forcing, aerosols, air pollutants, and clouds among others have huge environmental and economic consequences.
One of the major aims is to use remote sensing observations to better understand the conditions occurring in the real world, with a particular emphasis on the Global South and developing nations, areas which have fewer resources and access to high-quality observations, and areas which are at the same time experiencing immense changes in development, emissions, and to a larger extent climate impacts. By including observations from remote sensing, different parts of the atmosphere, different time scales, and different observations of what actually occurs in-situ is possible, leading to a more focused and relevant ability to constrain and understand the actual impacts of both anthropogenic and natural changes on the atmosphere, the environment, and the climate. The inclusion of all forcings, including both natural and anthropogenic, without respect to boarder or reporting bias, and over long periods of time, allows for new insights into the way in which the atmosphere is changing. This in turn allows for new concepts of finding ways to improve or make more detailed impact, attribution, and mitigation studies.
This focus issue aims to broadly solicit studies which use remotely sensed measurements in connection with or impacting upon the environment. We aim to be broad and inclusive in our definitions and welcome satellite remote sensing, ground remote sensing, aircraft remote sensing, drone remote sensing, and remote sensing across all active and passive platforms and all different wavelengths of the spectrum. The second aspect of the focus issue is that these remote sensing observations must be directly tied into the environment and its analysis, but again aim to be broad and inclusive.
We welcome studies directly monitoring air pollutants, aerosols, greenhouse gasses, and clouds, as well as other studies which focus more broadly or generally, such as how changes in the environment or climate impact the ability to observe the atmosphere, attribution studies, emissions inversions from top-down approaches, mitigation studies, and more. Impact studies including but not limited to health, visibility and economy are also welcome. Finally, studies also demonstrating new ways, techniques, or observations capable of merging, integrating, or analysing changes in the environment, both on average and over extreme events are also most welcome.
Specific topics of interest include, but are not limited to:
- Remote Sensing of Air Pollution
- Remote Sensing of Greenhouse Gasses
- Remote Sensing of Aerosols and Clouds
- Atmospheric Environmental Change and Impacts
- Radiative Forcing and Climate Change
- Emission and Mitigation
- Attribution of Sources and/or Impacts
Submission process
Focus issue research articles must be of the same format and meet the same publication criteria as regular research Letters in ERL. They are also subject to the same rigorous review process, high editorial standards and quality/novelty requirements. Read the about the journal page for more information before submitting.
For more comprehensive information on preparing your article for submission and the options for submitting your article, see our author guidelines.
All articles should be submitted using our online submission form, and the correct focus issue from the drop down box selected; "Focus Issue on Atmospheric Remote Sensing and Environmental Change".
We encourage authors to include a cover letter, or separate justification statement (in the 'File Upload' step) outlining how your article meets the requirements of ERL, and why it is suitable for consideration in the journal, and focus issue (see the 'special requirements' section).
Deadline for submissions
Submissions will be accepted until 29 February 2024 however submissions earlier than this date are encouraged. ERL will publish this focus collection incrementally, adding new articles to this webpage as and when they are accepted for publication following peer review. Therefore, authors who submit early will not have their manuscript held up waiting for other articles.
Publication charges
ERL is an open access journal, completely free to read, and is funded solely by article publication charges. Authors should therefore be aware of the article publication charge for accepted and published articles, including those in focus collections. Full details about the article charge can be found on the publication charges page and all available transformative agreements and discounts found here.
Participating Journals
Letter
A significant haze event occurred in northern China from 16 to 21 November 2022. This study analyzed the haze spatial evolution, and meteorological influences by integrating ground and satellite measurements. Most data were obtained using aerosol lidar and wind lidar observations in suburban (Nanjiao Observation Station, NJOS) and urban Beijing (Haidian Observation Station, HDOS). The observations at NJOS and HDOS indicate the presence of a distinct layer of haze restricted to a height of up to 1500 m above the surface. However, the aerosol intensity at HDOS was comparatively lower (aerosol extinction coefficient: 1.39 ± 0.27 km−1) than at NJOS (1.77 ± 0.38 km−1), with approximately one day of time lag in response to the southerly winds. Though NJOS and HDOS presented a similar wind stratification structure, the downdraft under 1000 m influenced the surface air quality were significantly different. The intense downdraft at the lower height at HDOS prevented the vertical upward diffusion of accumulated ground pollutants, whose effect was similar to that of the inversion layer. That led to a more stable increasing trend of PM2.5 at HDOS, with the shallowest planet boundary layer height of 242 m on 20 November. By contrast, NJOS in the transportation path was more regularly influenced by the southerly flow and presented cyclical PM2.5 concentration. This study shows downdraft in urban environments acting as an accelerator for urban episodic PM2.5 pollution, suggesting the complicated contribution from meteorological factors.
The ground surface temperature (GST) serves as a crucial indicator for understanding land-atmosphere mass and energy exchange. The shift from manual measurement to automated station for GST in China after 2002 introduced inconsistencies at certain stations, potentially distorting research findings. Here, daily automatedly observed GST from 2003 to 2017 at 615 selected meteorological stations were updated by constructing linear regression model based on manually observed air temperature (AT) and GST from 1960 to 2002. Then, the spatiotemporal variations of GST from 1960 to 2017 and its driving factors were investigated. Results indicated that: (1) the AT-GST linear regression model could effectively mitigate the inconsistency caused by the change of GST observation methods, enhancing data reliability. (2) GST in China showed little change from 1960–1980, but increased significantly across all regions from 1980 to 2000, with the increase rate slowed down except in the Qinghai–Tibet plateau (QTP) and southwest China after 2000. Notable GST increase is concentrated in colder regions, including the QTP, northeast (NEC), and northwest China (NWC). (3) Evapotranspiration (ET) and vapor pressure deficit were the primary drivers of annual GST variations at the regional scale, while their contributions to GST variations exhibited notable seasonal variability. Our findings could offer valuable scientific insights for addressing climate change, enhancing surface environmental models, and safeguarding ecological environments.
Ground-level ozone (O3), renowned for its adverse impacts on human health and crop production, has garnered significant attention from governmental and public sectors. To address the limitations posed by sparse and uneven ground-level O3 observations, this study proposes an innovative method for hourly full-coverage ground-level O3 estimation using machine learning. Meteorological data from National Centers for Environmental Prediction global forecasting system, satellite data from Fengyun-4 A(FY-4 A) and Ozone Monitoring Instrument, emission inventory from Multi-resolution Emission Inventory for China, and other auxiliary data are utilized as input variables, while ground-based O3 observations serve as the response variable. The method is applied on a monthly basis across China for the year 2022, resulting in the generation of an hourly full-coverage high-resolution (4 km) ground-level O3 estimation, termed ML-derived-O3. Cross-validation results demonstrate the robustness of ML-derived-O3 yielding a coefficient of determination (R2) of 0.96 (0.91) for sample-based (site-based) evaluations and a root-mean-square error (RMSE) of 9.22 (13.65) µg m−3. However, the date-based evaluation is less satisfactory due to the imbalanced training data, resulting from the pronounced daily variations in ground-level O3 concentrations. Nevertheless, the seasonal and hourly ML-derived-O3 exhibits high prediction accuracy, with R2 values surpassing 0.95 and RMSE remaining below 7.5 µg m−3. This study marks a significant milestone as the first successful attempt to obtain hourly full-coverage ground-level O3 data across China. The diurnal variation of ML-derived-O3 demonstrates high consistency with ground-based observations, irrespective of clear or cloudy days, effectively capturing ground-level O3 pollution exposure events. This novel estimation method will be employed to establish a long-term high spatial-temporal resolution ground-level O3 dataset, which holds valuable applications for air pollution monitoring and environmental health research in future endeavors.
The Asian summer monsoon (ASM) region is a key region transporting air to the upper troposphere (UT), significantly influencing the distribution and concentration of trace gases, including methane (CH4), an important greenhouse gas. We investigate the seasonal enhancement of CH4 in the UT over the ASM region, utilizing retrievals from the Atmospheric Infrared Sounder (AIRS), model simulations and in-situ measurements. Both the AIRS data and model simulation reveal a substantial enhancement in CH4 concentrations within the active monsoon region of up to 3%, referring to the zonal means, and of up to 6% relative to the pre-monsoon season. Notably, the spatial distribution of the CH4 plume demonstrates a southwestward shift in the AIRS retrievals, in contrast to the model simulations, which predict a broader enhancement, including a significant increase to the east. A cross-comparison with in-situ measurements, including AirCore measurements over the Tibetan Plateau and airline sampling across the ASM anticyclone (ASMA), favors the enhancement represented by model simulation. Remarkable CH4 enhancement over the west Pacific is also evidenced by in-situ data and simulation as a dynamical extension of the ASMA. Our findings underscore the necessity for cautious interpretation of satellite-derived CH4 distributions, and highlight the critical role of in-situ data in anchoring the assimilation of CH4.
Roadside air pollution is one of the serious air pollution problems in urban areas. Even though roadside air pollution has been reported to cause adverse human health impacts, the spatial distribution of roadside air pollution in a large urban agglomeration has yet to be fully assessed. This study aimed to analyse roadside fine particulate matter (PM2.5) pollution and the population exposure in 11 cities in the Pearl River Delta (PRD) region of China. We developed satellite-retrieval algorithms with dark target method, vector support machine model and random forest model to retrieve the spatial distribution of PM2.5 at an ultra-high-spatial-resolution (30 m) based on 30 m Landsat-8 L1 data. Our results show that the retrieved PM2.5 had a promising consistency with PM2.5 measurements at general and roadside stations (R2 = 0.86; RMSE = 7.72 µg m−3). Moreover, on average, the roadside PM2.5 in Dongguan, Foshan, and Guangzhou was relatively higher (up to 107.60 µg m−3) whereas that in Hong Kong was relatively lower (up to 30.40 µg m−3). The roadside PM2.5 pollution typically occurred in roads for motorized vehicles i.e. motorway, trunk, primary and secondary road. Our results also show that roadside PM2.5 was up to 17% higher in holidays than in workdays in all the PRD cities except Hong Kong that showed roadside PM2.5 higher in workdays than in holidays. The population-weighted PM2.5 decreased with increasing distances from roads in every PRD city, and population-weighted PM2.5 was estimated to be up to 22% higher at roadsides than at distances of 1500 m away from roads. This study pinpointed the seriousness of roadside air pollution in the PRD region.
The upper-tropospheric carbonaceous aerosol layer (TCAL) represents the increase of aerosols in the upper-troposphere. It was first discovered over Asia but was found in this study to also occur over South America and Africa. The TCALs over three regions typically exist during the strong deep convection season, with the Asian, South American, and African TCALs showing peak intensity during July–August, October–December, and November–December, respectively. Over Asia, the TCAL has the highest altitude and widest spread due to strongest deep convection and upper-troposphere anticyclonic system. TCAL intensity is highest in South America maybe due to heaviest pollutant emissions. Anthropogenic pollution from India and western China produces two Asian TCAL centers, whereas widespread wildfires result in single centers over South America and Africa. TCAL radiative effect at the top of the atmosphere has warming effects over Asia (+0.23 W m−2), whereas cooling effects perform over South America (−0.54 W m−2) and Africa (−0.20 W m−2) owing to its altitude and the divergent strengths of black-carbon absorption and organic-carbon scattering.
The severe aerosol pollution in East Asia has been a focus of much research. In Japan, the environmental quality standard (EQS) for PM2.5 was established in 2009 (daily average, 35 μg m−3; annual average, 15 μg m−3), and its achievement rate was below 50% during the early 2010s. Then, the PM2.5 concentration gradually decreased, the achievement rate improved, and the EQS for PM2.5 was finally achieved (100%) in fiscal year (FY) 2021. Because transboundary aerosol pollution is an important factor in Japanese air quality, here we analysed the long-term dataset of the satellite-measured fine-mode aerosol optical depth (AODf) over the East Asian ocean to reveal the changes in the transboundary aerosol over East Asia. Overall, a decrease in AODf was seen over the entire East Asian ocean during the period analysed. A gradual declining trend in AODf was measured (−4% to −5%/year over the adjacent ocean around Japan) and corresponded well to the trend in PM2.5 concentration observed in Japan (−5.3%/year) during FY2010–FY2021. Due to the domestic contribution in Japan, the negative trend was slightly greater for Japanese PM2.5 concentration than for AODf over the adjacent ocean around Japan, and we concluded that the main reason for the dramatic air quality improvement in PM2.5 in Japan was driven by the improvement of transboundary aerosol pollution over East Asia. In addition, the 12 year analysis period (FY2010 to FY2021) was divided into three parts: stagnation (FY2010 to FY2014), in which PM2.5 and AODf remained the same as they were in FY2010; improvement (FY2015 to FY2018), in which PM2.5 and AODf declined dramatically; and achievement (FY2019 to FY2021), in which PM2.5 and AODf declined further.
Anthropogenic nitrogen oxide (NOx) emissions are closely associated with human activities. In recent years, global human activity patterns have changed significantly owing to the COVID‐19 epidemic and international energy crisis. However, their effects on NOx emissions are not yet fully understood. In this study, we developed a two-step inversion framework using NO2 observations from the TROPOMI satellite and the GEOS-Chem global atmospheric chemical transport model, and inferred global anthropogenic NOx emissions from 2019 to 2022, focusing on China, the United States (U.S.), and Europe. Our results indicated an 1.68% reduction in NOx emissions in 2020 and a 5.72% rebound in 2021 across all regions. China rebounded faster than the others, surpassing its 2019 levels by July 2020. In 2022, emissions declined in all regions, driven mainly by the Omicron variant, energy shortages, and clean energy policies. Our findings provide valuable insights for the development of effective future emission management strategies.
Tropospheric ozone pollution poses a major environmental challenge in China. As its primary natural source, Stratosphere-to-Troposphere Transport (STT) has been recognized as a significant contributor to tropospheric ozone in western, northeastern, and eastern China. However, the extent of STT's influence on southeastern China has been less studied due to data limitations. Using a recently available one-year dataset of ozonesonde observations from a regional background station, we find that STT contributes significantly to tropospheric and surface ozone elevation in southeastern China. Our results show that STT plays a more substantial role in shaping tropospheric ozone during spring than previously believed, accounting for over 30% of ozone concentrations above 4 km. Without the stratospheric contribution, the spring seasonal peak almost disappears. STT can also significantly influence ozone concentrations at the surface. For example, a distinct ozone profile was observed on 4 May 2022, with a notable increase in tropospheric ozone. This tropospheric ozone increase was caused by a STT event triggered by a robust horizontal trough and subsequent southward movement of subtropical jets in the upper troposphere. According to a stratospheric tracer derived from an atmospheric chemistry model, this STT event contributed to 25%–30% of the surface ozone increase. Overall, this study highlights the important role of STT in driving tropospheric ozone variations, even in regions with comparatively lower ozone levels in southeastern China.