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In recent decades, the economy of China has developed rapidly, but this has brought widespread damage to the environment, which forces us to explore a sustainable, green, economic development model. Therefore, it is particularly necessary to clarify the relationship between economic development and environmental pollution. In this paper, we used satellite remote sensing tropospheric NO2 vertical column density (VCD) as an air quality indicator; the total exports, total imports, and industrial electricity consumption as the economic indicators; and the wind speed, temperature, and planetary boundary layer height as the meteorological factors to perform a Generalized Additive Modeling (GAM) analysis. By deducing the influence of meteorological factors, the relationship between economic indicators and the air quality indicator can be determined. When total exports increased by one billion USD (United States Dollar), the tropospheric NO2 VCDs of Nanjing and Suzhou increased by about 15% and 6%, respectively. The tropospheric NO2 VCDs of Suzhou increased by about 5% when the total imports increased by one billion USD. In addition, when the industrial electricity consumption increased by one billion kWh, the tropospheric NO2 VCDs of Nanjing, Suzhou and Xuzhou increased by about 25%, 12%, and 59%, respectively. This study provides a method to quantify the contribution of economic growth to air pollution, which is helpful for better understanding of the relationship between economic development and air quality. [ABSTRACT FROM AUTHOR]

In this paper, an operational forecasting and daily assessment system of air quality is presented. This new system is thought of as a Copernicus-CAMS downstream national service, able to develop and implement a service for air quality forecasting and monitoring in the Italian domain, running every day on the National territory. The system is being developed on behalf of a cooperation between Agenzia Spaziale Italiana (ASI) and Sistema Nazionale Protezione Ambiente (SNPA). SNPA is the network between Istituto Superiore per la Protezione e Ricerca Ambientale (ISPRA) and the Regional Environmental Agencies (ARPAs). The objective of the cooperation is to provide full operation service in terms of continuity, sustainability, and availability of the air quality forecast and evaluation services at the national level. The system forecasts and analyzes air quality throughout Italy, with a focus on Italian regions, for the principal pollutants: Particulate matter with diameter smaller than 10 μm (PM10), ozone (O3), and nitrogen dioxide (NO2). It includes a Chemical Transport Model (CTM) nested with the Copernicus Atmosphere Monitoring Service (CAMS) global model and data from the air quality monitoring stations in Italy. The system, under public control and based on open software, is now under testing. To date, it is able to deliver free open data, which is available to environmental agencies and citizens. The data are delivered both as maps and graphs, and as numerical data, useful for providing boundary conditions to local–high resolution-air quality models or for developing customized services. In this work, a downscaling application to a regional nested domain highlights how the new air quality forecasting system gains better results than the Copernicus-CAMS system. [ABSTRACT FROM AUTHOR]

Monitoring particulate matter with aerodynamic diameters of less than 2.5 μm (PM2.5) is of great importance to assess its adverse effects on human health, especially densely populated regions. In this paper, an improved linear mixed effect model (LMEM) was developed. The model introduced meteorological variable, column water vapor (CWV), which has as the same resolution as satellite-derived aerosol optical thickness (AOT), to enhance PM2.5 estimation accuracy by considering spatiotemporal consistency of CWV and AOT. The model was implemented to urban agglomeration of Chengdu Plain during 2015. The results show that model accuracy has been improved significantly compared to linear regression model (R2 = 0.49), with R2 of 0.81 and root mean squared prediction error (RMSPE) of 15.47 μg/m3, mean prediction error (MPE) of 11.09 μg/m3, and effectively revealed the characteristics of spatiotemporal variations PM2.5 level across the study area: The PM2.5 level is higher in the central and southern areas with dense population, while it is lower in the northwest and southwest mountain areas; and the PM2.5 level is higher during autumn and winter, while it is lower during spring and summer. The product data in this paper are valuable for local government pollution monitoring, public health research, and urban air quality control. [ABSTRACT FROM AUTHOR]

Dust storms are of great importance to climate change, air quality, and human health. In this study, a complete application frame of integrating hybrid methods based on multi-source data is proposed for remote sensing monitoring and process analysis of dust storms. In the frame, horizontal spatial distribution of dust intensity can be mapped by optical remote sensing products such as aerosol optical depth (AOD) from MODIS; the vertical spatial distribution of dust intensity by LIDAR satellite remote sensing products such as AOD profile from CALIPSO; geostationary satellite remote sensing products such as Chinese Fengyun or Japanese Himawari can achieve high-frequency temporal distribution information of dust storms. More detailed process analysis of dust storms includes air quality analysis supported by particulate matter (PM) data from ground stations and the dust emission trace and transport pathways from HYSPLIT back trajectory driven by meteorological data from the Global Data Assimilation System (GDAS). The dust storm outbreak condition of the source location can be proved by precipitation data from the WMO and soil moisture data from remote sensing products, which can be used to verify the deduced emission trace from HYSPLIT. The proposed application frame of integrating hybrid methods was applied to monitor and analyze a very heavy dust storm that occurred in northern China from 14–18 March 2021, which was one of the most severe dust storms in recent decades. Results showed that the dust storm event could be well monitored and analyzed dynamically. It was found that the dust originated in western Mongolia and northwestern China and was then transmitted along the northwest–southeast direction, consequently affected the air quality of most cities of northern China. The results are consistent with the prior research and showed the excellent potential of the integration of the hybrid methods in monitoring dust storms. [ABSTRACT FROM AUTHOR]

The objective of this paper is to use remote sensing to measure on-road emissions and to examine the impact and usefulness of additional measurement devices at three sites. Supplementing remote sensing device (RSD) equipment with additional equipment increased the capture rate by almost 10%. Post-processing of raw data is essential to obtain useful and accurate information. A method is presented to identify vehicles with excessive emission levels (high emitters). First, an anomaly detection method is applied, followed by identification of cold start operating conditions using infrared vehicle profiles. Using this method, 0.6% of the vehicles in the full (enhanced) RSD data were identified as high emitters, of which 35% are likely in cold start mode where emissions typically stabilize to low hot running emission levels within a few minutes. Analysis of NOx RSD data confirms that poor real-world NOx performance of Euro 4/5 light-duty diesel vehicles observed around the world is also evident in Australian measurements. This research suggests that the continued dieselisation in Australia, in particular under the current Euro 5 emission standards and the more stringent NO2 air quality criteria expected in 2020 and 2025, could potentially result in local air quality issues near busy roads. [ABSTRACT FROM AUTHOR]

The tropospheric vertical column density of NO2 (Trop NO2 VCD) can be obtained using satellite remote sensing, but it has been discovered that the Trop NO2 VCD is affected by uncertainties such as the cloud fraction, terrain reflectivity, and aerosol optical depth. A certain error occurs in terms of data inversion accuracy, necessitating additional ground observation verification. This study uses surface NO2 mass concentrations from the China National Environmental Monitoring Center (CNEMC) sites in Jiangsu Province, China in 2019 and the Trop NO2 VCD measured by MAX-DOAS, respectively, to verify the Trop NO2 VCD product (daily and monthly average data), that comes from the TROPOspheric Monitoring Instrument (TROPOMI) and Ozone Monitoring Instrument (OMI). The results show that the spatial distributions of NO2 in TROPOMI and OMI exhibit a similar tendency and seasonality, showing the characteristics of being high in spring and winter and low in summer and autumn. On the whole, the concentration of NO2 in the south of Jiangsu Province is higher than that in the north. The Pearson correlation coefficient (r) between the monthly average TROPOMI VCD NO2 and the CNEMC NO2 mass concentration is 0.9, which is greater than the r (0.78) between OMI and CNEMC; the r (0.69) between TROPOMI and the MAX-DOAS VCD NO2 is greater than the r (0.59) between OMI and the MAX-DOAS. As such, the TROPOMI is better than the previous generation of OMI at representing the spatio-temporal distribution of NO2 in the regional scope. On the other hand, the uncertainties of the satellite products provided in this study can constrain regional air quality forecasting models and top-down emission inventory estimation. [ABSTRACT FROM AUTHOR]

Intensive animal agriculture is an important part of the US and North Carolina's (NC's) economy. Large emissions of ammonia (NH3) gas emanate from the handling of animal wastes at these operations contributing to the formation of fine particulate matter (PM2.5) around the state causing a variety of human health and environmental effects. The objective of this research is to provide the relationship between ammonia, aerosol optical depth and meteorology and its effect on PM2.5 concentrations using satellite observations (column ammonia and aerosol optical depth (AOD)) and ground-based meteorological observations. An observational-based multiple linear regression model was derived to predict ground-level PM2.5 during the summer months (JJA) from 2008–2017 in New Hanover County, Catawba County and Sampson County. A combination of the Cumberland and Johnston County models for the summer was chosen and validated for Duplin County, NC, then used to predict Sampson County, NC, PM2.5 concentrations. The model predicted a total of six 24 h exceedances over the nine-year period. This indicates that there are rural areas of the state that may have air quality issues that are not captured for a lack of measurements. Moreover, PM2.5 chemical composition analysis suggests that ammonium is a major component of the PM2.5 aerosol. [ABSTRACT FROM AUTHOR]

High-spatial-resolution air quality (AQ) mapping is important for identifying pollution sources to facilitate local action. Some of the most populated cities in the world are not equipped with the infrastructure required to monitor AQ levels on the ground and must rely on other sources, such as satellite derived estimates, to monitor AQ. Current satellite-data-based models provide AQ mapping on a kilometer scale at best. In this study, we focus on producing hundred-meter-scale AQ maps for urban environments in developed cities. We examined the feasibility of an image-based object-detection analysis approach using very high-spatial-resolution (2.5 m) commercial satellite imagery. We fed the satellite imagery to a deep neural network (DNN) to learn the association between visual urban features and air pollutants. The developed model, which solely uses satellite imagery, was tested and evaluated using both ground monitoring observations and land-use regression modeled PM2.5 and NO2 concentrations over London, Vancouver (BC), Los Angeles, and New York City. The results demonstrate a low error with a total RMSE < 2 µg/m3 and highlight the contribution of specific urban features, such as green areas and roads, to continuous hundred-meter-scale AQ estimations. This approach offers promise for scaling to global applications in developed and developing urban environments. Further analysis on domain transferability will enable application of a parsimonious model based merely on satellite images to create hundred-meter-scale AQ maps in developing cities, where current and historical ground data are limited. [ABSTRACT FROM AUTHOR]

In order to monitor nighttime particular matter (PM) air quality in urban area, a back propagation neural network (BP neural network) inversion model is established, using low-light radiation data from the day/night band (DNB) of the Visible Infrared Imaging Radiometer Suite (VIIRS) aboard the Suomi National Polar-orbiting Partnership (S-NPP) satellite. The study focuses on the moonless and cloudless nights in Beijing during March-May 2015. A test is carried out by selecting surface PM2.5 data from 12 PM2.5 automatic monitoring stations and the corresponding night city light intensity from DNB. As indicated by the results, the linear correlation coefficient (R) between the results and the corresponding measured surface PM2.5 concentration is 0.91, and the root-mean-square error (RMSE) is 14.02 μg/m³ with the average of 59.39 μg/m³. Furthermore, the BP neural network model shows better accuracy when air relative humility ranges from 40% to 80% and surface PM2.5 concentration exceeds 40 μg/m³. The study provides a superiority approach for monitoring PM2.5 air quality from space with visible light remote sensing data at night. [ABSTRACT FROM AUTHOR]

Smoke from wildfires in Siberia often affects air quality over vast territories of the Northern hemisphere during the summer. Increasing fire emissions also affect regional and global carbon balance. To estimate annual carbon emissions from wildfires in Siberia from 2002–2020, we categorized levels of fire intensity for individual active fire pixels based on fire radiative power data from the standard MODIS product (MOD14/MYD14). For the last two decades, estimated annual direct carbon emissions from wildfires varied greatly, ranging from 20–220 Tg C per year. Sporadic maxima were observed in 2003 (>150 Tg C/year), in 2012 (>220 Tg C/year), in 2019 (~180 Tg C/year). However, the 2020 fire season was extraordinary in terms of fire emissions (~350 Tg C/year). The estimated average annual level of fire emissions was 80 ± 20 Tg C/year when extreme years were excluded from the analysis. For the next decade the average level of fire emissions might increase to 250 ± 30 Tg C/year for extreme fire seasons, and to 110 ± 20 Tg C/year for moderate fire seasons. However, under the extreme IPCC RPC 8.5 scenario for Siberia, wildfire emissions might increase to 1200–1500 Tg C/year by 2050 if there were no significant changes in patterns of vegetation distribution and fuel loadings. [ABSTRACT FROM AUTHOR]

This study reports the likely real-world effects of fleet replacement with electric vehicles (EVs) and higher efficiency EURO 6 vehicles on the exhaustive emissions of NOx, PM, and CO2 in the seven boroughs of the West Midlands (WM) region, UK. National fleet composition data, local EURO distributions, and traffic compositions were used to project vehicle fleet compositions for different roads in each borough. A large dataset of real-world emission factors including over 90,000 remote-sensing measurements, obtained from remote sensing campaigns in five UK cities, was used to parameterize the emission profiles of the studied scenarios. Results show that adoption of the fleet electrification approach would have the highest emission reduction potential on urban roads in WM boroughs. It would result in maximum reductions ranging from 35.0 to 37.9%, 44.3 to 48.3%, and 46.9 to 50.3% for NOx, PM, and CO2, respectively. In comparison, the EURO 6 replacement fleet scenario would lead to reductions ranging from 10.0 to 10.4%, 4.0 to 4.2%, and 6.0 to 6.4% for NOx, PM, and CO2, respectively. The studied mitigation scenarios have higher efficacies on motorways compared to rural and urban roads because of the differences in traffic fleet composition. The findings presented will help policymakers choose climate and air quality mitigation strategies. [ABSTRACT FROM AUTHOR]

Several studies have found rising ambient particulate matter (PM 2.5 ) concentrations in urban areas across developing countries. For setting mitigation policies source-contribution is needed, which is calculated mostly through computationally intensive chemical transport models or manpower intensive source apportionment studies. Data based approach that use remote sensing datasets can help reduce this challenge, specially in developing countries which lack spatially and temporally dense air quality monitoring networks. Our objective was identifying relative contribution of urban emission sources to monthly PM 2.5 ambient concentrations and assessing whether urban expansion can explain rise of PM 2.5 ambient concentration from 2001 to 2015 in 15 Indian cities. We adapted the Intergovernmental Panel on Climate Change's (IPCC) emission framework in a land use regression (LUR) model to estimate concentrations by statistically modeling the impact of urban growth on aerosol concentrations with the help of remote sensing datasets. Contribution to concentration from six key sources (residential, industrial, commercial, crop fires, brick kiln and vehicles) was estimated by inverse distance weighting of their emissions in the land-use regression model. A hierarchical Bayesian approach was used to account for the random effects due to the heterogeneous emitting sources in the 15 cities. Long-term ambient PM 2.5 concentration from 2001 to 2015, was represented by a indicator R (varying from 0 to 100), decomposed from MODIS (Moderate Resolution Imaging Spectroradiometer) derived AOD (aerosol optical depth) and angstrom exponent datasets. The model was trained on annual-level spatial land-use distribution and technological advancement data and the monthly-level emission activity of 2001 and 2011 over each location to predict monthly R. The results suggest that above the central portion of a city, concentration due to primary PM 2.5 emission is contributed mostly by residential areas (35.0 ± 11.9%), brick kilns (11.7 ± 5.2%) and industries (4.2 ± 2.8%). The model performed moderately for most cities (median correlation for out of time validation was 0.52), especially when assumed changes in seasonal emissions for each source reflected actual seasonal changes in emissions. The results suggest the need for policies focusing on emissions from residential regions and brick kilns. The relative order of the contributions estimated by this study is consistent with other recent studies and a contribution of up to 42.8 ± 14.1% is attributed to the formation of secondary aerosol, long-range transport and unaccounted sources in surrounding regions. The strength of this approach is to be able to estimate the contribution of urban growth to primary aerosols statistically with a relatively low computation cost compared to the more accurate but computationally expensive chemical transport based models. This remote sensing based approach is especially useful in locations without emission inventory. [ABSTRACT FROM AUTHOR]