Integration of Remote Sensing and Social Sensing Data in a Deep Learning Framework for Hourly Urban PM2.5 Mapping.
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ABSTRACT:
SUBMITTER: Shen H
PROVIDER: S-EPMC6861963 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Retrieving historical fine particulate matter (PM2.5) data is key for evaluating the long-term impacts of PM2.5 on the environment, human health and climate change. Satellite-based aerosol optical depth has been used to estimate PM2.5, but estimations have largely been undermined by massive missing values, low sampling frequency and weak predictive capability. Here, using a novel feature engineering approach to incorporate spatial effects from meteorological data, we developed a robust LightGBM model that predicts PM2.5 at an unprecedented predictive capacity on hourly (R2 = 0.75), daily (R2 = 0.84), monthly (R2 = 0.88) and annual (R2 = 0.87) timescales. By taking advantage of spatial features, our model can also construct hourly gridded networks of PM2.5. This capability would be further enhanced if meteorological observations from regional stations were incorporated. Our results show that this model has great potential in reconstructing historical PM2.5 datasets and real-time gridded networks at high spatial-temporal resolutions. The resulting datasets can be assimilated into models to produce long-term re-analysis that incorporates interactions between aerosols and physical processes.
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Project description:There is concern regarding the heterogeneity of exposure to airborne particulate matter (PM) across urban areas leading to negatively biased health effects models. New, low-cost sensors now permit continuous and simultaneous measurements to be made in multiple locations. Measurements of ambient PM were made from October to April 2015-2016 and 2016-2017 to assess the spatial and temporal variability in PM and the relative importance of traffic and wood smoke to outdoor PM concentrations in Rochester, NY, USA. In general, there was moderate spatial inhomogeneity, as indicated by multiple pairwise measures including coefficient of divergence and signed rank tests of the value distributions. Pearson correlation coefficients were often moderate (~50% of units showed correlations >0.5 during the first season), indicating that there was some coherent variation across the area, likely driven by a combination of meteorological conditions (wind speed, direction, and mixed layer heights) and the concentration of PM2.5 being transported into the region. Although the accuracy of these PM sensors is limited, they are sufficiently precise relative to one another and to research grade instruments that they can be useful is assessing the spatial and temporal variations across an area and provide concentration estimates based on higher-quality central site monitoring data.
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Project description:The Denver Aerosol Sources and Health (DASH) study was designed to evaluate associations between PM2.5 species and sources and adverse human health effects. The DASH study generated a five-year (2003-2007) time series of daily speciated PM2.5 concentration measurements from a single, special-purpose monitoring site in Denver, CO. To evaluate the ability of this site to adequately represent the short term temporal variability of PM2.5 concentrations in the five county Denver metropolitan area, a one year supplemental set of PM2.5 samples was collected every sixth day at the original DASH monitoring site and concurrently at three additional sites. Two of the four sites, including the original DASH site, were located in residential areas at least 1.9 km from interstate highways. The other two sites were located within 0.3 km of interstate highways. Concentrations of elemental carbon (EC), organic carbon (OC), and 58 organic molecular markers were measured at each site. To assess spatial variability, site pairs were compared using the Pearson correlation coefficient (r) and coefficient of divergence (COD), a statistic that provides information on the degree of uniformity between monitoring sites. Biweekly co-located samples collected from July 2004 to September 2005 were also analyzed and used to estimate the uncertainty associated with sampling and analytical measurement for each species. In general, the two near-highway sites exhibited higher concentrations of EC, OC, polycyclic aromatic hydrocarbons (PAHs), and steranes than did the more residential sites. Lower spatial heterogeneity based on r and COD was inferred for all carbonaceous species after considering their divergence and lack of perfect correlations in co-located samples. Ratio-ratio plots combined with available gasoline- and diesel-powered motor vehicle emissions profiles for the region suggested a greater impact to high molecular weight (HMW) PAHs from diesel-powered vehicles at the near-highway sites and a more uniformly distributed impact to ambient hopanes from gasoline-powered motor vehicles at all four sites.
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Project description:In China, severe haze is a major public health concern affecting residents' health and well-being. This study used hourly air quality monitoring data from 285 cities in China to analyze the effect of green coverage (GC) and other economic variables on the incremental PM2.5 concentration (?PM2.5) during peak hours. To detect possible non-linear and interaction effect between predictive variables, a kernel-based regularized least squares (KRLS) model was used for empirical analysis. The results show that there was considerable heterogeneity between cities regarding marginal effect of GC on ?PM2.5, which could potentially be explained by different seasons, latitude, urban maintenance expenditure (UE), real GDP per capita (PG), and population density (PD). Also described in this study, in cities with high UE, the growth of GC, PG, and PD always remain a positive impact on mitigation of haze pollution. This shows that government expenditure on urban maintenance can reduce or mitigate the environmental pollution from economic development. In addition, the influence of other urban elements on air quality had also been analyzed so that different combinations of mitigation policies are proposed for different regions in this study to meet the mitigation targets.
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Project description:The Central Plains Urban Agglomeration (CPUA) is the largest region in central China and suffers from serious air pollution. To reveal the spatiotemporal variations and the sources of fine particulate matter (PM2.5, with an aerodynamic diameter of smaller than 2.5 μm) concentrations of CPUA, multiple and transdisciplinary methods were used to analyse the collected millions of PM2.5 concentration data. The results showed that during 2017 ~ 2020, the yearly mean concentrations of PM2.5 for CPUA were 68.3, 61.5, 58.7, and 51.5 μg/m3, respectively. The empirical orthogonal function (EOF) analysis suggested that high PM2.5 pollution mainly occurred in winter (100.8 μg/m3, 4-year average). The diurnal change in PM2.5 concentrations varied slightly over the season. The centroid of the PM2.5 concentration moved towards the west over time. The spatial autocorrelation analysis indicated that PM2.5 concentrations exhibited a positive spatial autocorrelation in CPUA. The most polluted cities distributed in the northern CPUA (Handan was the centre) formed a high-high agglomeration, and the cities located in the southern CPUA (Xinyang was the centre) formed a low-low agglomeration. The backward trajectory model and potential source contribution function were employed to discuss the regional transportation of PM2.5. The results demonstrated that internal-region and cross-regional transport of anthropogenic emissions were all important to PM2.5 pollution of CPUA. Our study suggests that joint efforts across cities and regions are necessary.Supplementary informationThe online version contains supplementary material available at 10.1007/s11869-022-01178-z.
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Project description:IntroductionDespite the London Underground (LU) handling on average 2.8 million passenger journeys per day, the characteristics and potential health effects of the elevated concentrations of metal-rich PM2.5 found in this subway system are not well understood.MethodsSpatial monitoring campaigns were carried out to characterise the health-relevant chemical and physical properties of PM2.5 across the LU network, including diurnal and day-to-day variability and spatial distribution (above ground, depth below ground and subway line). Population-weighted station PM2.5 rankings were produced to understand the relative importance of concentrations at different stations and on different lines.ResultsThe PM2.5 mass in the LU (mean 88??g?m-3, median 28??g?m-3) was greater than at ambient background locations (mean 19??g?m-3, median 14??g?m-3) and roadside environments in central London (mean 22??g?m-3, median 14??g?m-3). Concentrations varied between lines and locations, with the deepest and shallowest submerged lines being the District (median 4??g?m-3) and Victoria (median 361??g?m-3 but up to 885??g?m-3). Broadly in agreement with other subway systems around the world, sampled LU PM2.5 comprised 47% iron oxide, 7% elemental carbon, 11% organic carbon, and 14% metallic and mineral oxides. Although a relationship between line depth and air quality inside the tube trains was evident, there were clear influences relating to the distance from cleaner outside air and the exchange with cabin air when the doors open. The passenger population-weighted exposure analysis demonstrated a method to identify stations that should be prioritised for remediation to improve air quality.ConclusionPM2.5 concentrations in the LU are many times higher than in other London transport Environments. Failure to include this environment in epidemiological studies of the relationship between PM2.5 and health in London is therefore likely to lead to a large exposure misclassification error. Given the significant contribution of underground PM2.5 to daily exposure, and the differences in composition compared to urban PM2.5, there is a clear need for well-designed studies to better understand the health effects of underground exposure.
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Project description:In January 2020, China implemented strict lockdown measures due to the invasion of the new coronavirus, which led to a sharp decline in the contribution of anthropogenic fine particulate matter (PM2.5). The special period of COVID-19, especially in Hubei where the epidemic was the most severe, provides excellent research conditions for studying the contribution of anthropogenic activities to PM2.5 concentrations. We used an optimized deep learning model to predict PM2.5 concentration during the epidemic period in the cities of Hubei Province. The contributions of local anthropogenic activities to PM2.5 pollution were obtained by contrasting the predicted results with actual site observations. However, a strange phenomenon was revealed that Yichang, a city with low local anthropogenic contribution to PM2.5, was found to have severe haze in winter conflicting with our previous expectations. After further research, we found that an increased conversion of secondary aerosols caused by long-distance transport of pollutant gases from the northern region is the main cause of winter haze pollution in this city. This finding highlights the importance of joint regional prevention and control of air pollution.
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Project description:The volatility profiles of PM2.5 semi-volatile compounds and relationships to the oxidative potential of urban airborne particles were investigated in central Los Angeles, CA. Ambient and thermodenuded fine (PM2.5) particles were collected during both warm and cold seasons by employing the Versatile Aerosol Concentration Enrichment System (VACES) combined with a thermodenuder. When operated at 50 °C and 100 °C, the VACES/thermodenuder system removed about 50% and 75% of the PM2.5 volume concentration, respectively. Most of the quantified PM2.5 semi-volatile species including organic carbon (OC), water soluble organic carbon (WSOC), polycyclic aromatic hydrocarbons (PAHs), organic acids, n-alkanes, and levoglucosan, as well as inorganic ions (i.e., nitrate, sulfate, and ammonium) exhibited concentration losses in the ranges of 40-66% and 67-92%, respectively, as the thermodenuder temperature increased to 50 °C and 100 °C. Species in the PM2.5 such as elemental carbon (EC) and inorganic elements (including trace metals) were minimally impacted by the heating process - thus can be considered refractory. On average, nearly half of the PM2.5 oxidative potential (as measured by the dichlorodihydrofluorescein (DCFH) alveolar macrophage in vitro assay) was associated with the semi-volatile species removed by heating the aerosols to only 50 °C, highlighting the importance of this quite volatile compartment to the ambient PM2.5 toxicity. The fraction of PM2.5 oxidative potential lost upon heating the aerosols to 100 °C further increased to around 75-85%. Furthermore, we document statistically significant correlations between the PM2.5 oxidative potential and different semi-volatile organic compounds originating from primary and secondary sources, including OC (Rwarm, and Rcold) (0.86, and 0.74), WSOC (0.60, and 0.98), PAHs (0.88, and 0.76), organic acids (0.76, and 0.88), and n-alkanes (0.67, and 0.83) in warm and cold seasons, respectively, while a strong correlation between oxidative potential and levoglucosan, a tracer of biomass burning, was observed only during the cold season (Rcold=0.81).
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Project description:Emerging evidence has showed that exposure to airborne particulate matter (PM) with an aerodynamic diameter less than 2.5 μm (PM2.5) is associated with neurodegeneration. Our previous studies in vitro found that PM2.5 exposure causes primary neurons damage through activating microglia. However, the molecular mechanism of microglia-mediated neurotoxicity remains to elucidate. In this study, five groups (N = 13 or 10) of six-week-old male C57BL/6 mice were daily exposed to PM2.5 (0.1 or 1 mg/kg/day body weight), Chelex-treated PM2.5 (1 mg/kg/day body weight), PM2.5 (1 mg/kg/day body weight) plus CB-839 (glutaminase inhibitor), or deionized water by intranasal instillation for 28 days, respectively. Compared with the control groups, We found that PM2.5 triggered reactive oxygen species (ROS) generation and microglia activation evidenced by significant increase of ionized calcium binding adaptor molecule-1 (IBa-1) staining in the mouse olfactory bulbs (OB). Data from transmission electron microscope (TEM) images and Western blot analysis showed that PM2.5 significantly increased extracellular vesicles (EVs) release from OB or murine microglial line BV2 cells, and glutaminase C (GAC) expression and glutamate generation in isolated OB and BV2 cells. However, treatment with N-acetylcysteine (NAC) or CB-839 significantly diminished the number of EVs and the expression of GAC and abolished PM2.5-induced neurotoxicity. These findings provide new insights that PM2.5 induces oxidative stress and microglia activation through its metal contents and glutaminase-containing EVs in OBs, which may serve as a potential pathway/mechanism of excessive glutamate generation in PM2.5-induced neurotoxicity.
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