Ground-level Monitoring Research Articles

Prenatal Exposure to Ambient Air Pollution and Cerebral Palsy

Air pollution is associated with structural brain changes, disruption of neurogenesis, and neurodevelopmental disorders. The association between prenatal exposure to ambient air pollution and risk of cerebral palsy (CP), which is the most common motor disability in childhood, has not been thoroughly investigated. To evaluate the associations between prenatal residential exposure to ambient air pollution and risk of CP among children born at term gestation in a population cohort in Ontario, Canada. Population-based cohort study in Ontario, Canada using linked, province-wide health administrative databases. Participants were singleton full term births (≥37 gestational weeks) born in Ontario hospitals between April 1, 2002, and March 31, 2017. Data were analyzed from January to December 2022. Weekly average concentrations of ambient fine particulate matter with a diameter 2.5 μm (PM2.5) or smaller, nitrogen dioxide (NO2), and ozone (O3) during pregnancy assigned by maternal residence reported at delivery from satellite-based estimates and ground-level monitoring data. CP cases were ascertained by a single inpatient hospitalization diagnosis or at least 2 outpatient diagnoses for children from birth to age 18 years. The present study included 1 587 935 mother-child pairs who reached term gestation, among whom 3170 (0.2%) children were diagnosed with CP. The study population had a mean (SD) maternal age of 30.1 (5.6) years and 811 745 infants (51.1%) were male. A per IQR increase (2.7 μg/m3) in prenatal ambient PM2.5 concentration was associated with a cumulative hazard ratio (CHR) of 1.12 (95% CI, 1.03-1.21) for CP. The CHR in male infants (1.14; 95% CI, 1.02-1.26) was higher compared with the CHR in female infants (1.08; 95% CI, 0.96-1.22). No specific window of susceptibility was found for prenatal PM2.5 exposure and CP in the study population. No associations or windows of susceptibility were found for prenatal NO2 or O3 exposure and CP risk. In this large cohort study of singleton full term births in Canada, prenatal ambient PM2.5 exposure was associated with an increased risk of CP in offspring. Further studies are needed to explore this association and its potential biological pathways, which could advance the identification of environmental risk factors of CP in early life.

Joint estimation of PM2.5 and O3 concentrations using a hybrid model in Beijing-Tianjin-Hebei, China

The distribution data of fine particulate matter (PM2.5) and ozone (O3) concentrations at high spatiotemporal resolutions play a pivotal role in monitoring and collectively combating atmospheric PM2.5 and O3 pollution, thereby enhancing the quality of human habitation environments. This study integrates reanalysis, satellite observations, and ground-level monitoring data to develop a GLMM-DF (generalized linear mixed effects model-deep forest) hybrid model for joint estimation of PM2.5 and O3 concentrations in the Beijing-Tianjin-Hebei region. The model effectively considers the spatiotemporal heterogeneity inherent in PM2.5 and O3 and their nonlinear relationships with the estimating variables. The cross-validation results indicate that the GLMM-DF model exhibits high estimation accuracy. For PM2.5 estimation, the coefficient of determination (R2), mean absolute error (MAE), and root mean square Error (RMSE) are 0.949, 5.81 μg m−3, and 9.42 μg m−3, respectively. Regarding O3 estimation, the R2, MAE, and RMSE are 0.966, 7.77 μg m−3, and 11.82 μg m−3, respectively. Furthermore, the model demonstrates favorable spatiotemporal performance, with the PM2.5-O3 interaction contributing to enhancing model performance. Utilizing this model for mapping, we analyzed the spatiotemporal trends of PM2.5 and O3 in the Beijing-Tianjin-Hebei region, uncovering several intriguing phenomena.

Prenatal Exposure to Air Pollution and Respiratory Distress in Term Newborns: Results from the MIREC Prospective Pregnancy Cohort.

Respiratory distress is the leading cause of neonatal morbidity and mortality worldwide, and prenatal exposure to air pollution is associated with adverse long-term respiratory outcomes; however, the impact of prenatal air pollution exposure on neonatal respiratory distress has not been well studied. We examined associations between prenatal exposures to fine particular matter () and nitrogen dioxide () with respiratory distress and related neonatal outcomes. We used data from the Maternal-Infant Research on Environmental Chemicals (MIREC) Study, a prospective pregnancy cohort () recruited in the first trimester from 10 Canadian cities. Prenatal exposures to () and () were estimated using land-use regression and satellite-derived models coupled with ground-level monitoring and linked to participants based on residential location at birth. We calculated odds ratios (ORs) and 95% confidence intervals (CIs) for associations between air pollution and physician-diagnosed respiratory distress in term neonates in hierarchical logistic regression models adjusting for detailed maternal and infant covariates. Approximately of newborns experienced respiratory distress. Neonates received clinical interventions including oxygen therapy (6%), assisted ventilation (2%), and systemic antibiotics (3%). Two percent received multiple interventions and 4% were admitted to the neonatal intensive care unit (NICU). Median and concentrations during pregnancy were and , respectively. Prenatal exposures to air pollution were not associated with physician-diagnosed respiratory distress, oxygen therapy, or NICU admissions. However, exposures were strongly associated with assisted ventilation (OR per increase in ; 95% CI: 1.02, 1.35), multiple clinical interventions (OR per increase in ; 95% CI: 1.07, 1.26), and systemic antibiotics, (OR per increase in ; 95% CI: 1.04, 1.21). These associations were consistent across exposure periods-that is, during prepregnancy, individual trimesters, and total pregnancy-and robust to model specification. exposure was associated with administration of systemic antibiotics (OR per 1-ppb increase in ; 95% CI: 1.00, 1.06). Prenatal exposures to increased the risk of severe respiratory distress among term newborns. These findings support the development and prioritization of public health and prenatal care strategies to increase awareness and minimize prenatal exposures to air pollution. https://doi.org/10.1289/EHP12880.

Shifts in biodiversity and physical structure of seagrass beds across 5 decades at Carriacou, Grenadines.

Caribbean seagrass beds are facing increasing anthropogenic stress, yet comprehensive ground-level monitoring programs that capture the structure of seagrass communities before the 1980s are rare. We measured the distribution of seagrass beds and species composition and abundance of seagrass and associated macroalgae and macroinvertebrates in 3 years over a 47-year period (1969, 1994, 2016) at Carriacou, Granada, an area not heavily impacted by local human activity. Seagrass cover and physical parameters of fringing beds were measured in transects at high (HWE) and low wave energy (LWE) sites; frequency of occurrence of all species, and biomass and morphology of seagrasses, were measured at 100 m2 stations around the island. Losses in nearshore seagrass cover occurred at HWE but not LWE sites between 1969 and 2016 and were associated with increases in the seagrass-free inshore zone (SFI) and erosional scarps within beds. Total biomass did not vary across years although there were progressive changes in seagrass composition: a decline in the dominant Thalassia testudinum and concomitant increase in Syringodium filiforme, and establishment of invasive Halophila stipulacea in 2016 at LWE sites. Species richness and diversity of the seagrass community were highest in 1994, when 94% of macroalgae (excluding Caulerpa) were most abundant, and sea urchins were least abundant, compared to 1969 and 2016. Multivariate statistical analyses showed differences in community composition across the 3 years that were consistent with trends in urchin abundance. Increases in SFI and scarp number in seagrass beds at HWE sites occurred mainly after 1994 and likely were related to increased wave forcing following degradation of offshore coral reefs between 1994 and 2016. Our observations suggest that landward migration of seagrass beds with rapidly rising sea level in future will not be realized in reef-protected seagrass beds at Carriacou barring reversal in the processes that have caused reef flattening.

Seasonality and intensity of airborne Boletus-type spores in relation to land use and weather pattern

Forests are a natural source of airborne bolete spores. The timing of sporulation and its intensity as well as the dispersal of airborne spores and in consequence their concentrations depend in particular on the type of land use determining the availability of matter on which they develop and on meteorological factors. The aim of this study was to perform a spatial and temporal analysis of the occurrence of Boletus-type spores in the warm temperate climate of the Northern Hemisphere. An assumption was made that the spore concentrations depend on the type of land cover and weather conditions. The volumetric method was applied to investigate differences in spore concentrations and using spore traps installed at different heights and at locations with different land cover types. Boletus-type spores occurred in the air at high concentrations in late summer and in the autumn. The season start dates and maximum concentrations did not differ significantly between sites and seasons, but the season intensity varied. Higher spore concentrations were usually found in the region with a larger proportion of green areas, including forests. An analysis of the diurnal cycles showed that within 24 h spore concentration reached high levels twice, which was especially noticeable in ground level monitoring. Air temperature and air humidity were the main weather factors affecting the occurrence of airborne spores. This research indicates that when studying the effects of different factors on the concentration of airborne basidiospores, many environmental elements should be analyzed, including the characteristics of habitats in which basidiomycetes grow. Climate, weather, geobotany, and land use type should be taken into account in analysis and interpretation of aeromycological phenomena.

Characteristics and mechanism of a persistent ozone pollution event in Pearl River Delta induced by typhoon and subtropical high

Ozone (O3) is a significant air pollutant in the Pearl River Delta (PRD) region, with severe pollution events primarily associated with the Western Pacific Subtropical High (WPSH) and typhoon periphery synoptic weather patterns. To investigate the continuity and differences in O3 pollution processes influenced by these patterns, we analyze a severe O3 pollution event in Zhuhai from September 10 to 13, 2022. The study collects vertical profiles of O3 from the differential absorption O3 lidar and wind fields observed by the Doppler wind profile lidar. We utilize ground-level monitoring data from national ambient air quality monitoring network and meteorological stations to analyze diurnal variations of air pollutant concentrations and ground-level meteorological conditions. The study reveals that both the WPSH and the typhoon periphery create favorable meteorological conditions for severe O3 pollution events. The highest temperatures during the WPSH and typhoon periphery stages are 32.1 °C and 37.5 °C, respectively. During the WPSH control stage, the combined effects of the inversion layer and light static wind hinder O3 dispersion. The ground-level O3 concentration reaches a maximum of 106 ppb. Then the typhoon periphery control stage, the ground-level O3 peak increases to 170 ppb, showing a 60% increase compared to the WPSH stage. O3 horizontal and vertical transport processes play a crucial role in the O3 episode during this stage. The northerly winds predominantly transport pollutants from upstream urban clusters to the local area, and intense downdrafts facilitate the downward transport of O3 from the residual layer. The study investigates the mechanisms of three-dimensional O3 transport within the boundary layer influenced by the WPSH and typhoon periphery in the PRD region. The results contribute valuable insights to pollution events in the PRD region under typical synoptic weather patterns.

The impact of different aerosol layering conditions on the high-resolution MODIS/MAIAC AOD retrieval bias: The uncertainty analysis

The Eastern Mediterranean/Middle East (EMME) region belongs to one of the most polluted and vulnerable to climate change related areas in the world. To monitor these changes, comprehensive set of measurements need to be conducted. Ground-level monitoring sites provide continuous measurements, yet their spatial coverage in EMME is very limited. Satellite data largely expand spatial coverage, however, retrieval accuracy over arid regions with bright surface background, mixed with urban and industrial sources, remains challenging. In this study, we analyzed the agreement between aerosol optical depth (AOD) data from the Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm at 1-km spatial resolution and corresponding ground-based Aerosol Robotic Network (AERONET) measurements, using data from September 2019 to October 2022 in Tel Aviv, Israel. We identified all overestimation and underestimation MAIAC AOD measurements, relative to AERONET, and used multiwavelength polarization lidar observations of vertical aerosol profiles to characterize these conditions. Our findings suggest that under clear atmospheric conditions (AOD¡0.12), MAIAC overestimation prevails, while under dusty (AOD¿0.3), and partially cloudy conditions, MAIAC underestimation prevails. We found that dust component (high particle depolarization ratio, and low Angstrom exponent) is often presented in cases of underestimation, while a single layer overestimation cases typically related to anthropogenic pollution. The two-layers overestimation cases tend to have marine aerosols at the bottom and mixed pollution sources at the top. Our study highlights the importance of studying the different layering conditions that largely bias the MAIAC AOD retrieval accuracy. This knowledge is highly important since AOD is widely used as input variable in numerous modeling studies and air quality applications and rarely prepossessed for such a bias.

The Effects of Wildfire Smoke on Asthma and Allergy.

To review the recent literature on the effects of wildfire smoke (WFS) exposure on asthma and allergic disease, and on potential mechanisms of disease. Spatiotemporal modeling and increased ground-level monitoring data are allowing a more detailed picture of the health effects of WFS exposure to emerge, especially with regard to asthma. There is also epidemiologic and some experimental evidence to suggest that WFS exposure increases allergic predisposition and upper airway or sinonasal disease, though much of the literature in this area is focused more generally on PM2.5 and is not specific for WFS. Experimental evidence for mechanisms includes disruption of epithelial integrity with downstream effects on inflammatory or immune pathways, but experimental models to date have not consistently reflected human disease in this area. Exposure to WFS has an acute detrimental effect on asthma. Potential mechanisms are suggested by in vitro and animal studies.

Children's exposure to indoor and outdoor black carbon and particulate matter air pollution at school in Rwanda, Central-East Africa

Schoolchildren are a high-risk population, and their exposure to air pollution at school has been documented in high-income countries. As a result, school siting policies to protect children from air pollution have been established in those countries. Such policies are, however, non-existent in Africa where the air pollution problem is growing and gravely under-studied due to the lack of funding to install reliable ground-level monitoring networks and lack of air quality standards. The present study, the first of its kind to be conducted in Africa, measured Black Carbon (BC) and its related sources: fossil fuels (BCff) and biomass burning (BCbb). BC and fine particles (PM2.5) were measured simultaneously indoors and outdoors at schools over a 12-month period in Rwanda. The annual concentrations of BC in classrooms (8.15 µg/m3) and outdoor schoolyards (9.22 µg/m3) were higher than simultaneous concentrations recorded at Kigali urban background site (7.78 μg m3). The annual mean PM2.5 concentrations in classrooms and outdoor school were more than eight times higher than the World Health Organization's safe limits. The results demonstrated a more than two-fold increase in the concentrations of BCff during drop-off hours compared to off-peak hours, indicating the dominant contribution of vehicles queuing on the school premises. However, higher indoor than outdoor BC levels were recorded in some instances, 2 hours after an outdoor peak during drop-off times. The exposure peaks inside the classrooms were more pronounced for BC than PM2.5.The highest reduction of 24% and 19% were observed for BCff during weekends and school holidays in classrooms compared to regular days. No reduction was observed for BCbb concentrations. This study shows that BC and PM2.5 levels are considerably higher in schoolyards, and that further investigation of air pollution exposure at schools is warranted in Africa to promote improved drop-off/pick-up behaviours at and around school environments.

Influence of land-sea breeze on PM_{2.5} prediction in central and southern Taiwan using composite neural network

PM_{2.5} prediction plays an important role for governments in establishing policies to control the emission of excessive atmospheric pollutants to protect the health of citizens. However, traditional machine learning methods that use data collected from ground-level monitoring stations have reached their limit with poor model generalization and insufficient data. We propose a composite neural network trained with aerosol optical depth (AOD) and weather data collected from satellites, as well as interpolated ocean wind features. We investigate the model outputs of different components of the composite neural network, concluding that the proposed composite neural network architecture yields significant improvements in overall performance compared to each component and the ensemble model benchmarks. The monthly analysis also demonstrates the superiority of the proposed architecture for stations where land-sea breezes frequently occur in the southern and central Taiwan in the months when land-sea breeze dominates the accumulation of PM_{2.5}.

Assessment of aerosols over five cities of Angola based on MERRA–2 reanalysis data

The use of Aerosol Optical Depth (AOD) has been proven as an alternative to the traditional ground-level monitoring of air quality in many countries across the world. Therefore, this study based on MERRA-2 data aims: (i) to characterize the spatiotemporal and component variations of aerosols in the atmosphere over the capital cities (Luanda, Sumbe, Benguela, Huambo and Lubango) of the five most densely populated provinces of Angola from 2010 to 2020; (ii) to analyze the relationship between the monthly mean variation of the total AOD and the meteorological parameters (precipitation, temperature, wind speed, and relative humidity) in those five cities; and (iii) to assess the influence of emissions from the Nyamuragira volcano, located in the Democratic Republic of Congo, on the air quality at the five cities. The most significant contribution to the total AOD was derived from organic carbon, in all the cities, whereby the highest values (0.19–0.23) were in Luanda. Ranges of sulphates across the coastal cities were higher when compared to the interior cities caused by the emissions inventory data. The value of AOD in all the cities increased and reached the peak during the dry season. The HYSPLIT model showed that air masses from Nyamuragira at various heights in November 2011 reached Luanda and Sumbe, and CALIPSO could confirm the existence of volcanic aerosols in this same period. This study allowed to conclude that the variability of AOD loading depends on seasons and regions, thus providing a little more information about the matter.

Long-term Exposure to Oxidant Gases and Mortality: Effect Modification by PM 2.5 Transition Metals and Oxidative Potential.

Background:Populations are simultaneously exposed to outdoor concentrations of oxidant gases (i.e., O3 and NO2) and fine particulate air pollution (PM2.5). Since oxidative stress is thought to be an important mechanism explaining air pollution health effects, the adverse health impacts of oxidant gases may be greater in locations where PM2.5 is more capable of causing oxidative stress.Methods:We conducted a cohort study of 2 million adults in Canada between 2001 and 2016 living within 10 km of ground-level monitoring sites for outdoor PM2.5 components and oxidative potential. Ox exposures (i.e., the redox-weighted average of O3 and NO2) were estimated using a combination of chemical transport models, land use regression models, and ground-level data. Cox proportional hazards models were used to estimate associations between 3-year moving average Ox and mortality outcomes across strata of transition metals and sulfur in PM2.5 and three measures of PM2.5 oxidative potential adjusting for possible confounding factors.Results:Associations between Ox and mortality were consistently stronger in regions with elevated PM2.5 transition metal/sulfur content and oxidative potential. For example, each interquartile increase (6.27 ppb) in Ox was associated with a 14.9% (95% CI = 13.0, 16.9) increased risk of nonaccidental mortality in locations with glutathione-related oxidative potential (OPGSH) above the median whereas a 2.50% (95% CI = 0.600, 4.40) increase was observed in regions with OPGSH levels below the median (interaction P value <0.001).Conclusion:Spatial variations in PM2.5 composition and oxidative potential may contribute to heterogeneity in the observed health impacts of long-term exposures to oxidant gases.

A Low-Cost Multi-Purpose IoT Sensor for Biologging and Soundscape Activities.

The rapid expansion in miniaturization, usability, energy efficiency, and affordability of Internet of Things (IoT) sensors, integrated with innovations in smart capability, is greatly increasing opportunities in ground-level monitoring of ecosystems at a specific scale using sensor grids. Surrounding sound is a powerful data source for investigating urban and non-urban ecosystem health, and researchers commonly use robust but expensive passive sensors as monitoring equipment to capture it. This paper comprehensively describes the hardware behind our low-cost, small multipurpose prototype, capable of monitoring different environments (e.g., remote locations) with onboard processing power. The device consists of a printed circuit board, microprocessor, local memory, environmental sensor, microphones, optical sensors and LoRa (Long Range) communication systems. The device was successfully used in different use cases, from monitoring mosquitoes enhanced with optical sensors to ocean activities using a hydrophone.

Analysis of PM2.5 Variations Based on Observed, Satellite-Derived, and Population-Weighted Concentrations

Fine particulate matter (PM2.5), which can cause adverse human health effects, has been proven as the first air pollutant in China. In situ observations with ground-level monitoring and satellite-based concentrations have been used to analyze the variations in PM2.5. However, variation analyses based on these two kinds of measurement have mainly focused on the concentration itself and ignored the effects on the population. Therefore, this study not only investigated these two kinds of measurements, but also performed weighted population analyses to study the variations in PM2.5. Firstly, daily models of timely structure adaptive modeling (TSAM) were constructed to simulate satellite-derived PM2.5 levels from January 2013 to December 2016. Secondly, population-weighted concentrations were calculated based on TSAM-derived PM2.5 surfaces. Finally, observed, TSAM-derived, and population-weighted concentrations were used to analyze the variations in PM2.5. The results showed the different importance of various input parameters; AOD had the highest rank. Additionally, TSAM models demonstrated good performance, fitting R ranging from 0.86 to 0.91, and validating R from 0.82 to 0.89. According to the air quality standard in China, TSAM-derived PM2.5 showed that the increase in area lower than Level II was 29.03% and the increase in population was only 14.81%. This indicates that the air quality exhibited an overall improvement in spatial perspective, but some areas with high population density showed a relatively low improvement due to uneven distributions in China. The population-weighted PM2.5 concentration could better represent the health threats of air pollutants compared with in situ observations.

Toward Global Estimation of Ground-Level NO2 Pollution With Deep Learning and Remote Sensing

Air pollution is a central environmental problem in countries around the world. It contributes to climate change through the emission of greenhouse gases, and adversely impacts the health of billions of people. Despite its importance, detailed information about the spatial and temporal distribution of pollutants is complex to obtain. Ground-level monitoring stations are sparse, and approaches for modeling air pollution rely on extensive datasets which are unavailable for many locations. We introduce three techniques for the estimation of air pollution to overcome these limitations: 1) a baseline localized approach that mimics conventional land-use regression through gradient boosting; 2) an OpenStreetMap (OSM) approach with gradient boosting that is applicable beyond regions covered by detailed geographic datasets; and 3) a remote sensing-based deep learning method utilizing multiband imagery and trace-gas column density measurements from satellites. We focus on the estimation of nitrogen dioxide (NO₂), a common anthropogenic air pollutant with adverse effects on the environment and human health. Our local baseline model achieves strong results with a mean absolute error (MAE) of 5.18 &#x00B1; <inline-formula> <tex-math notation="LaTeX">$0.16~\mu \text {g/m}^{3}$ </tex-math></inline-formula> NO₂. Substituting localized inputs with OSM leads to a degraded performance (MAE 7.22 &#x00B1; 0.14) but enables NO₂ estimation at a global scale. The proposed deep learning model on remote sensing data combines high accuracy (MAE 5.5 &#x00B1; 0.14) with global coverage and heteroscedastic uncertainty quantification. Our results enable the estimation of surface-level NO₂ pollution with high spatial resolution for any location on Earth. We illustrate this capability with an out-of-distribution test set on the US westcoast. Code<xref ref-type="fn" rid="fn1">¹</xref> and data<xref ref-type="fn" rid="fn2">²</xref> are publicly available.

Critical Time Windows for Air Pollution Exposure and Birth Weight in a Multicity Canadian Pregnancy Cohort.

Maternal prenatal exposure to air pollution has been associated with adverse birth outcomes. However, previous studies focused on a priori time intervals such as trimesters reported inconsistent associations. We investigated time-varying vulnerability of birth weight to fine particulate matter (PM2.5) and nitrogen dioxide (NO2) using flexible time intervals. We analyzed 1,300 live, full-term births from Maternal-Infant Research on Environmental Chemicals, a Canadian prospective pregnancy cohort spanning 10 cities (2008-2011). Daily PM2.5 and NO2 concentrations were estimated from ground-level monitoring, satellite models, and land-use regression, and assigned to participants from pre-pregnancy through delivery. We developed a flexible two-stage modeling method-using a Bayesian Metropolis-Hastings algorithm and empirical density threshold-to identify time-dependent vulnerability to air pollution without specifying exposure periods a priori. This approach identified critical windows with varying lengths (2-363 days) and critical windows that fell within, or straddled, predetermined time periods (i.e., trimesters). We adjusted the models for detailed infant and maternal covariates. Critical windows associated with reduced birth weight were identified during mid- to late-pregnancy for both PM2.5 and NO2: -6 g (95% credible interval: -11, -1 g) and -5 g (-10, -0.1 g) per µg/m3 PM2.5 during gestational days 91-139 and 249-272, respectively; and -3 g (-5, -1 g) per ppb NO2 during days 55-145. We used a novel, flexible selection method to identify critical windows when maternal exposures to air pollution were associated with decrements in birth weight. Our results suggest that air pollution impacts on fetal development may not be adequately captured by trimester-based analyses.

Identification of NOx hotspots from oversampled TROPOMI NO2 column based on image segmentation method

Satellite-based measures of NO2 have become increasingly available for resolving the limitation on insufficient spatial and temporal coverage of ground-level monitoring networks. Oversampled NO2 column density can obtain more detailed features of NO2 column with a spatial resolution as high as 2 km × 2 km, while it is still challenging to identify hotspots of NOx pollution plume in city-scale due to background interference. In this study, we proposed a method for detecting the NOx hotspot grids from oversampled satellite NO2 column based on the image segmentation method, and identifying major source types using Term frequency-inverse document frequency (TF-IDF). A fractal model was used to evaluate and eliminate the background portion of the NO2 column and an adaptive threshold method was adopted to identify the region of interest (ROI) of local hotspot NO2 column. Hot-grid index, counting the frequency of NO2 hotspot ROI in each grid, was conducted to identify the hotspot grids. TF-IDF was used to semantically analyze the major source types of NO2 hotspot grids. Taking Central and Eastern China as the studied domain, the hotspot grids of NO2 and the relevant major source types were identified based on the proposed method. The major non-road mobile sources (such as Beijing Capital International Airport), industrial areas (such as Caofeidian Industrial Park) and urban areas were clearly distinguished. The power plant, Coke and Iron and Steel were identified as major source types in the whole year in the corresponding NOx hotspot grids. Notably, the identification of hotspot grids indicated a higher probability of a local high-intensity NOx pollution plume rather than a quantitative NOx emission; the key source types were the semantic keywords in hotspot grids, which does not mean there were no other exiting emission sources. This proposed method has strong implications on rapidly identifying the NOx hotspot grids based on oversampled TROPOMI NO2 column and the list of industrial enterprises.

Estimating PM2.5 concentrations via random forest method using satellite, auxiliary, and ground-level station dataset at multiple temporal scales across China in 2017.

Fine particulate matter with aerodynamic diameters less than 2.5μm (PM2.5) poses adverse impacts on public health and the environment. It is still a great challenge to estimate high-resolution PM2.5 concentrations at moderate scales. The current study calibrated PM2.5 concentrations at a 1 km resolution scale using ground-level monitoring data, Aerosol Optical Depth (AOD), meteorological data, and auxiliary data via Random Forest (RF) model across China in 2017. The three ten-folded cross-validations (CV) methods including sample-based, time-based, and spatial-based validation combined with Coefficient Square (R2), Root-Mean-Square Error (RMSE), and Mean Predictive Error (MPE) have been used for validation at different temporal scales in terms of daily, monthly, heating seasonal, and non-heating seasonal. Finally, the distribution map of PM2.5 concentrations was illustrated based on the RF model. Some findings were achieved. The RF model performed well, with a relatively high sample-based cross-validation R2 of 0.74, a low RMSE of 16.29μg×m-3, and a small MPE of -0.282μg×m-3. Meanwhile, the performance of the RF model in inferring the PM2.5 concentrations was well at urban scales except for Chengyu (CY). North China, the CY urban agglomeration, and the northwest of China exhibited relatively high PM2.5 pollution features, especially in the heating season. The robustness of the RF model in the present study outperformed most statistical regression models for calibrating PM2.5 concentrations. The outcomes can supply an up-to-date scientific dataset for epidemiological and air pollutants exposure risk studies across China.

Spatiotemporal estimation of satellite-borne and ground-level NO2 using full residual deep networks

Compared with the limited capability of ground-level monitoring, remote sensing provides useful image data at a moderate or high spatial or temporal resolution with global coverage for monitoring of air pollutants, e.g., aerosol optical depth (AOD) observations from the MODIS for fine particulate matter (PM2.5) and Ozone Monitoring Instrument (OMI) nitrogen dioxide (NO2) vertical columns for ground-level NO2 concentration. However, the extensive nonrandom missingness of OMI-NO2 data (e.g., an approximate per-pixel missing proportion of 59% for mainland China in 2015) due to cloud contamination or high reflectance limits applicability of these data in estimation of ground-level NO2. This paper proposes the use of a full residual deep learning method to impute missing satellite-borne NO2 data (OMI-NO2) and to estimate (map) ground-level NO2 with uncertainty (coefficient of variation) at a high spatial (1 × 1 km2) and temporal (daily) resolution. For the large study region (mainland China except Hainan Province), the presented method achieved robust performance with a stable learning efficiency (mean test R2: 0.98 with a small standard deviation of 0.01; mean test RMSE: 0.42 × 1015 molecules/cm2) for imputation of OMI-NO2. In the model, the coordinates and elevation were used to capture the spatial variability of the OMI-NO2 columns, and fused meteorological grid data and planetary boundary layer height and ozone data from GEOS-FP were used to capture spatiotemporal variability of OMI-NO2. The evaluation with ground in situ NO2 measurements showed considerable contribution of the complete (raw observed and imputed) OMI-NO2 columns, meteorology and traffic variables to inference of ground-level NO2 (test R2: 0.82; test RMSE: 8.80 μg/m3). The complete grids of OMI-NO2 columns showed natural and smooth spatial transitions between the raw observed and imputed values. The surfaces of predicted NO2 concentration not only showed consistent distributions with OMI-NO2 at a regional and temporal scale, but also presented local spatial gradients of ground-level NO2. OMI-NO2 can be downscaled and imputed to be used as an important predictor to improve the estimation of high-resolution ground-level NO2. The reliable estimates of ground-level NO2 concentration with uncertainty can reduce the bias in estimates of NO2 exposure and subsequently evaluations of its health effects.

Comparison of Satellite-retrieved NO2 Vertical Column Density with Ground-level NO2 concentration in a provincial scale region

Satellite remote sensing will have certain errors in data retrieval accuracy, which requires further ground observation verification. This paper examines and verifies 2019 Jiangsu provincial Ground-level monitoring data and TROPOMI data to evaluate the applicability of TROPOMI tropospheric NO2 Vertical Column Density (VCD) data and compares the results to OMI data analysis verification results. The results show that the correlation coefficient r between the TROPOMI NO2 data and Ground-level monitoring data at the monthly mean scale is as high as 0.9, and consistent seasonal cyclic changes are observed. The correlation between Ground-level monitoring data and OMI NO2 data is lower than TROPOMI (r=0.78 < 0.9). Compared to OMI satellites, the TROPOMI attains a smaller deviation in tropospheric NO2 monitoring. Jiangsu Province had the highest monthly average concentration in January, which were 38.67 ug/m3 (Ground-level), 17.35 × 1015 molec/cm2 (TROPOMI) and 20.04 × 1015 molec/cm2 (OMI). The lowest concentration in August was 13.39 ug/m3 (Ground-level), 5.2 × 1015 molec/cm2 (TROPOMI) and 7.03 × 1015 molec/cm2 (OMI).