Optimal Planning of Air Quality-Monitoring Sites for Better Depiction of PM Pollution across China.

A myriad of studies have attempted to use ground-level observations to obtain gap-free spatiotemporal variations of PM, in support of air quality management and impact studies. Statistical methods (machine learning, etc.) or numerical methods by combining chemical transport modeling and observations with data assimilation techniques have been typically applied, yet the significance of site placement has not been well recognized.

Synergistic data fusion of multimodal AOD and air quality data for near real-time full coverage air pollution assessment.

Data gaps in satellite aerosol optical depth (AOD) retrievals pose a huge challenge in near real-time air quality assessment. Here, we present a multimodal aerosol data fusion approach to integrate multisource AOD and air quality data for the generation of full coverage AOD maps at hourly resolution. Specifically, data gaps in each Himawari-8 AOD snapshot were partially filled by merging all available daytime AOD snapshots, and these partially gap-filled AOD maps were then fused with coarse yet spatially complete numerical AOD simulations to generate full coverage AOD imageries.

Impact of near-surface turbulence on PM concentration in Chengdu during the COVID-19 pandemic.

The role of meteorological conditions has long been recognized in modulating regional air quality. The impact of near-surface turbulence, nevertheless, remains poorly understood. To curb the spread of COVID-19, a variety of lockdown measures were implemented, providing us an unprecedented opportunity to examine the joint impact of emission control and meteorology on regional air quality.

Characteristics of Chemical Speciation in PM in Six Representative Regions in China.

A better knowledge of aerosol properties is of great significance for elucidating the complex mechanisms behind frequently occurring haze pollution events. In this study, we examine the temporal and spatial variations in both PM and its major chemical constituents using three-year field measurements that were collected in six representative regions in China between 2012 and 2014. Our results show that both PM and its chemical compositions varied significantly in space and time, with high PM loadings mainly observed in the winter.

Influence of COVID-19 lockdown overlapping Chinese Spring Festival on household PM in rural Chinese homes.

During the 2019 novel coronavirus (COVID-19) pandemic, many countries took strong lockdown policy to reduce disease spreading, resulting in mitigating the ambient air pollution due to less traffic and industrial emissions. However, limited studies focused on the household air pollution especially in rural area, the potential risk induced by indoor air pollution exposure was unknown during this period. This field study continuously measured real-time PM levels in kitchen, living room, and outdoor in the normal days (Period-1) and the days of COVID-19 lockdown overlapping the Chinese Spring Festival (Period-2) in rural homes in China.

Distinct spatiotemporal variation patterns of surface ozone in China due to diverse influential factors.

A better knowledge of surface ozone variations and the relevant influential factors is of great significance for controlling frequent ozone pollution events. In this study, we first examined the primary variation patterns of surface ozone in space and time across China via a clustering analysis on the basis of daily maximum 8h average surface ozone (MDA8) between 2015 and 2018. Statistical models were then established between MDA8 and a set of influential factors to pinpoint dominant factors contributing to regional MDA8 variations.

Advancing the prediction accuracy of satellite-based PM concentration mapping: A perspective of data mining through in situ PM measurements.

Ground-measured PM concentration data are oftentimes used as a response variable in various satellite-based PM mapping practices, yet few studies have attempted to incorporate ground-measured PM data collected from nearby stations or previous days as a priori information to improve the accuracy of gridded PM mapping. In this study, Gaussian kernel-based interpolators were developed to estimate prior PM information at each grid using neighboring PM observations in space and time. The estimated prior PM information and other factors such as aerosol optical depth (AOD) and meteorological conditions were incorporated into random forest regression models as essential predictor variables for more accurate PM mapping.

Spatiotemporal Associations between PM and SO as well as NO in China from 2015 to 2018.

Given the critical roles of nitrates and sulfates in fine particulate matter (PM) formation, we examined spatiotemporal associations between PM and sulfur dioxide (SO) as well as nitrogen dioxide (NO) in China by taking advantage of the in situ observations of these three pollutants measured from the China national air quality monitoring network for the period from 2015 to 2018. Maximum covariance analysis (MCA) was applied to explore their possible coupled modes in space and time. The relative contribution of SO and NO to PM was then quantified via a statistical modeling scheme.

Diagnosing atmospheric stability effects on the modeling accuracy of PM /AOD relationship in eastern China using radiosonde data.

Atmospheric stability significantly influences the accumulation and dispersion of air pollutants in the near-surface atmosphere, yet few stability metrics have been applied as predictors in statistical PM concentration mapping practices. In this study, eleven stability metrics were derived from radiosonde soundings collected in eastern China for the time period of 2015-2018 and then applied as independent predictors to explore their potential in favoring the prediction of PM. The statistical results show that the in situ PM concentration measurements correlated well with these stability metrics, especially at monthly and seasonal timescales.

Estimating Ground-Level Concentrations of Multiple Air Pollutants and Their Health Impacts in the Huaihe River Basin in China.

Air pollutants existing in the environment may have negative impacts on human health depending on their toxicity and concentrations. Remote sensing data enable researchers to map concentrations of various air pollutants over vast areas. By combining ground-level concentrations with population data, the spatial distribution of health impacts attributed to air pollutants can be acquired.

Spatiotemporal trend analysis for fine particulate matter concentrations in China using high-resolution satellite-derived and ground-measured PM data.

Atmospheric fine particulate matters (PM) have raised global concerns because of their markedly adverse effects on public health and environmental quality. In parallel with technological variations and social changes in the evolving industrialization pathways in China, there is an acute need to evaluate the long-term spatiotemporal trend of PM concentrations across China after years of elevation. Toward this end, an integrated high-resolution satellite-derived (1998-2016) and ground-measured (2015-2017) PM data base was applied.

Integrating multisensor satellite data merging and image reconstruction in support of machine learning for better water quality management.

Monitoring water quality changes in lakes, reservoirs, estuaries, and coastal waters is critical in response to the needs for sustainable development. This study develops a remote sensing-based multiscale modeling system by integrating multi-sensor satellite data merging and image reconstruction algorithms in support of feature extraction with machine learning leading to automate continuous water quality monitoring in environmentally sensitive regions. This new Earth observation platform, termed "cross-mission data merging and image reconstruction with machine learning" (CDMIM), is capable of merging multiple satellite imageries to provide daily water quality monitoring through a series of image processing, enhancement, reconstruction, and data mining/machine learning techniques.