Global Atmospheric Composition Needs from Future Ultraviolet–Visible–Near-Infrared (UV–Vis–NIR) NOAA Satellite Instruments

Presentations spanned a range of applications: the public health impacts of poor air quality and environmental justice;greenhouse gas measuring, monitoring, reporting, and verification (GHG MMRV);stratospheric ozone monitoring;and various applications of satellite observations to improve models, including data assimilation in global Earth system models. The combination of methane (CH4), carbon dioxide (CO2), carbon monoxide (CO), and NO2 retrievals can improve confidence in emissions inventories and model performance, and together these data products would be of use in future air quality management tools. The ability to retrieve additional trace gases (e.g., ethane, isoprene, and ammonia) in the thermal IR along with those measured in the UV–Vis–NIR region would be extremely useful for air quality applications, including source apportionment analysis (e.g., for oil/natural gas extraction, biogenic, and agricultural sources). Ground-level ozone is one of six criteria pollutants for which the EPA sets National Ambient Air Quality Standards (NAAQS) to protect against human health and welfare effects.

Performance of Bayesian Model Averaging (BMA) for Short-Term Prediction of PM10 Concentration in the Peninsular Malaysia

In preparation for the Fourth Industrial Revolution (IR 4.0) in Malaysia, the government envisions a path to environmental sustainability and an improvement in air quality. Air quality measurements were initiated in different backgrounds including urban, suburban, industrial and rural to detect any significant changes in air quality parameters. Due to the dynamic nature of the weather, geographical location and anthropogenic sources, many uncertainties must be considered when dealing with air pollution data. In recent years, the Bayesian approach to fitting statistical models has gained more popularity due to its alternative modelling strategy that accounted for uncertainties for all air quality parameters. Therefore, this study aims to evaluate the performance of Bayesian Model Averaging (BMA) in predicting the next-day PM10 concentration in Peninsular Malaysia. A case study utilized seventeen years' worth of air quality monitoring data from nine (9) monitoring stations located in Peninsular Malaysia, using eight air quality parameters, i.e., PM10, NO2, SO2, CO, O3, temperature, relative humidity and wind speed. The performances of the next-day PM10 prediction were calculated using five models' performance evaluators, namely Coefficient of Determination (R2), Index of Agreement (IA), Kling-Gupta efficiency (KGE), Mean Absolute Error (MAE), Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE). The BMA models indicate that relative humidity, wind speed and PM10 contributed the most to the prediction model for the majority of stations with (R2 = 0.752 at Pasir Gudang monitoring station), (R2 = 0.749 at Larkin monitoring station), (R2 = 0.703 at Kota Bharu monitoring station), (R2 = 0.696 at Kangar monitoring station) and (R2 = 0.692 at Jerantut monitoring station), respectively. Furthermore, the BMA models demonstrated a good prediction model performance, with IA ranging from 0.84 to 0.91, R2 ranging from 0.64 to 0.75 and KGE ranging from 0.61 to 0.74 for all monitoring stations. According to the results of the investigation, BMA should be utilised in research and forecasting operations pertaining to environmental issues such as air pollution. From this study, BMA is recommended as one of the prediction tools for forecasting air pollution concentration, especially particulate matter level.

Integral Assessment of Atmospheric Air Quality in the Largest Cities of Russia Based on TROPOMI (Sentinel-5P) Data for 2019–2020

This paper considers the level of atmospheric air pollution of the 20 largest cities in Russia in 2019–2020. The data used for the study is initially collected by a TROPOMI instrument (on the Sentinel-5P satellite), including measurements of carbon monoxide, formaldehyde, nitrogen dioxide, sulfur dioxide, and aerosol (aerosol index). The measurements were obtained using the cloud-based platform, Google Earth Engine, which presents L3 level data available for direct analysis. The Tropomi Air Quality Index (TAQI) integrates available TROPOMI measurements into a single indicator. The calculation results showed that most of the cities under consideration (15 out of 20) have a low or higher than usual level of pollution. Formaldehyde (35.7%) and nitrogen dioxide (26.4%) play the main role in the composition of pollution particles. A significant share is occupied by sulfur dioxide (16.4%). The contribution of carbon monoxide and aerosol averages 10.8 and 10.6%, respectively. Air pollution in cities is caused by both natural (wildfires, dust storms) and anthropogenic (seasonal migrations of the population, restrictions due to the COVID-19 pandemic) factors. Estimating atmospheric pollution levels in urban areas using an integral index based on remote data (such as TAQI) can be considered as a valuable information addition to existing ground-based measuring systems within the multisensory paradigm.

Assessment of COVID-19 Impacts on Air Quality in Ulaanbaatar, Mongolia, Based on Terrestrial and Sentinel-5P TROPOMI Data

The study aims to reveal the impact of three sequential strict-lockdowns of COVID-19 measures on the air pollutants including NO2, SO2, PM10, and PM2.5 in Ulaanbaatar, Mongolia during November 2020–February 2021 based on air quality network and satellite data. Based on measurements of automatic air quality sites in Ulaanbaatar, we found a substantial decrease in NO2 (up to 45%), PM10 (72%), and PM2.5 (59%) compared to the same periods in the previous five years. On the other hand, up to a threefold increase in SO2 concentration was seen. Compared to 2015–2020, the number of days exceeding the national air quality standard level of NO2 decreased by 55% during November 2020–February 2021. A similar trend was observed for PM10 and PM2.5 (30% and 14%, respectively). Conversely, days exceeding the national air quality standard level of SO2 increased by 58%. The third strict-lockdown exhibited significant reductions in pollutant concentrations. The percentage exceeding the national standard level for NO2, PM10, and PM2.5 constituted 23%, 50%, and 67% during the lockdown periods while it was 89%, 84%, and 91%, respectively, for the same periods in the previous five years. Even though Sentinel 5P-TROPOMI data do not fully reflect the above findings, they add valuable insights into the spatial pollution pattern during strict-lockdown and non-lockdown periods. The study demonstrates that measures taken during the strict-lockdown periods clearly influenced the values of daily patterns of NO2, PM10, and PM2.5 concentrations. On the contrary, it is important to note that SO2 concentration increased during the last two winter months after 2019.

Air quality impacts of COVID-19 lockdown measures detected from space using high spatial resolution observations of multiple trace gases from Sentinel-5P/TROPOMI

The aim of this paper is to highlight how TROPOspheric Monitoring Instrument (TROPOMI) trace gas data can best be used and interpreted to understand event-based impacts on air quality from regional to city scales around the globe. For this study, we present the observed changes in the atmospheric column amounts of five trace gases (NO2, SO2, CO, HCHO, and CHOCHO) detected by the Sentinel-5P TROPOMI instrument and driven by reductions in anthropogenic emissions due to COVID-19 lockdown measures in 2020. We report clear COVID-19-related decreases in TROPOMI NO2 column amounts on all continents. For megacities, reductions in column amounts of tropospheric NO2 range between 14 % and 63 %. For China and India, supported by NO2 observations, where the primary source of anthropogenic SO2 is coal-fired power generation, we were able to detect sector-specific emission changes using the SO2 data. For HCHO and CHOCHO, we consistently observe anthropogenic changes in 2-week-averaged column amounts over China and India during the early phases of the lockdown periods. That these variations over such a short timescale are detectable from space is due to the high resolution and improved sensitivity of the TROPOMI instrument. For CO, we observe a small reduction over China, which is in concert with the other trace gas reductions observed during lockdown;however, large interannual differences prevent firm conclusions from being drawn. The joint analysis of COVID-19-lockdown-driven reductions in satellite-observed trace gas column amounts using the latest operational and scientific retrieval techniques for five species concomitantly is unprecedented. However, the meteorologically and seasonally driven variability of the five trace gases does not allow for drawing fully quantitative conclusions on the reduction in anthropogenic emissions based on TROPOMI observations alone. We anticipate that in future the combined use of inverse modeling techniques with the high spatial resolution data from S5P/TROPOMI for all observed trace gases presented here will yield a significantly improved sector-specific, space-based analysis of the impact of COVID-19 lockdown measures as compared to other existing satellite observations. Such analyses will further enhance the scientific impact and societal relevance of the TROPOMI mission.

Analysis and Dynamic Monitoring of Indoor Air Quality Based on Laser-Induced Breakdown Spectroscopy and Machine Learning

The air quality of the living area influences human health to a certain extent. Therefore, it is particularly important to detect the quality of indoor air. However, traditional detection methods mainly depend on chemical analysis, which has long been criticized for its high time cost. In this research, a rapid air detection method for the indoor environment using laser-induced breakdown spectroscopy (LIBS) and machine learning was proposed. Four common scenes were simulated, including burning carbon, burning incense, spraying perfume and hot shower which often led to indoor air quality changes. Two steps of spectral measurements and algorithm analysis were used in the experiment. Moreover, the proposed method was found to be effective in distinguishing different kinds of aerosols and presenting sensitivity to the air compositions. In this paper, the signal was isolated by the forest, so the singular values were filtered out. Meanwhile, the spectra of different scenarios were analyzed via the principal component analysis (PCA), and the air environment was classified by K-Nearest Neighbor (KNN) algorithm with an accuracy of 99.2%. Moreover, based on the establishment of a high-precision quantitative detection model, a back propagation (BP) neural network was introduced to improve the robustness and accuracy of indoor environment. The results show that by taking this method, the dynamic prediction of elements concentration can be realized, and its recognition accuracy is 96.5%.

Assessment of air quality index (AQI) in Riyadh, Saudi Arabia

Air pollution is increasingly becoming a main environmental matter in the world. It can impact public health, weather, and climate. Riyadh's air pollution poses significant environmental and health risks. The study aimed to analyze Riyadh's current and future air quality by using AQI. Main six air pollutants are considered, nitrogen dioxide (NO2), sulfur dioxide (SO2), particulate matter (PM), ground level ozone (O3), and carbon dioxide (CO2), carbon monoxide (CO). Eleven air quality stations located throughout Riyadh assess the concentration of six standard pollutants daily. A comparison of air quality in Riyadh was done at the local, regional, and international levels. Furthermore, diverse factors such as meteorological seasons, working periods, and the COVID-19 period are taken into account. Industrial emissions, as well as contributions from mobile sources and wind-blown dust, appear to be the principal pollutant sources affecting Riyadh. The measured air quality components for all contaminants were found to be below standard. PM poses the greatest damage to the city's human health of all the pollutants studied. It can be found in practically all locations of air quality stations, even though CO and O3 levels in the city are not at alarming levels.

Stereoscopic Monitoring: A Promising Strategy to Advance Diagnostic and Prediction of Air Pollution

Monitoring and modeling/predicting air pollution are crucial to understanding the links between emissions and air pollution levels, to supporting air quality management, and to reducing human exposure. Yet, current monitoring networks and modeling capabilities are unfortunately inadequate to understand the physical and chemical processes above ground and to support attribution of sources. We highlight the need for the development of an international stereoscopic monitoring strategy that can depict three-dimensional (3D) distribution of atmospheric composition to reduce the uncertainties and to advance diagnostic understanding and prediction of air pollution. There are three reasons for the implementation of stereoscopic monitoring: 1) current observation networks provide only partial view of air pollution, and this can lead to misleading air quality management actions;2) satellite retrievals of air pollutants are widely used in air pollution studies, but too often users do not acknowledge that they have large uncertainties, which can be reduced with measurements of vertical profiles;and 3) air quality modeling and forecasting require 3D observational constraints. We call on researchers and policymakers to establish stereoscopic monitoring networks and share monitoring data to better characterize the formation of air pollution, optimize air quality management, and protect human health. Future directions for advancing monitoring and modeling/predicting air pollution are also discussed.

A Review of Selected Types of Indoor Air Purifiers in Terms of Microbial Air Contamination Reduction

Aims: With the ongoing pandemic and increased interest in measures to improve indoor air quality, various indoor air purifiers have become very popular and are widely used. This review presents the advantages and disadvantages of various types of technologies used in air purifiers in terms of reducing microbial contamination. Methods: A literature search was performed using Web of Science, Scopus, and PubMed, as well as technical organizations dealing with indoor air-quality to identify research articles and documents within our defined scope of interest. Relevant sections: The available literature data focus mainly on the efficiency of devices based on tests conducted in laboratory conditions with test chambers, which does not reflect the real dimensions and conditions observed in residential areas. According to a wide range of articles on the topic, the actual effectiveness of air purifiers is significantly lower in real conditions than the values declared by the manufacturers in their marketing materials as well as technical specifications. Conclusions: According to current findings, using indoor air purifiers should not be the only measure to improve indoor air-quality;however, these can play a supporting role if their application is preceded by an appropriate technical and environmental analysis considering the real conditions of its use.

Trends in secondary inorganic aerosol pollution in China and its responses to emission controls of precursors in wintertime

The Chinese government recently proposed ammonia (NH3) emission reductions (but without a specific national target) as a strategic option to mitigate fine particulate matter (PM2.5) pollution. We combined a meta-analysis of nationwide measurements and air quality modeling to identify efficiency gains by striking a balance between controlling NH3 and acid gas (SO2 and NOx) emissions. We found that PM2.5 concentrations decreased from 2000 to 2019, but annual mean PM2.5 concentrations still exceeded 35 µg m-3 at 74 % of 1498 monitoring sites during 2015–2019. The concentration of PM2.5 and its components were significantly higher (16 %–195 %) on hazy days than on non-hazy days. Compared with mean values of other components, this difference was more significant for the secondary inorganic ions SO42-, NO3-, and NH4+ (average increase 98 %). While sulfate concentrations significantly decreased over this period, no significant change was observed for nitrate and ammonium concentrations. Model simulations indicate that the effectiveness of a 50 % NH3 emission reduction for controlling secondary inorganic aerosol (SIA) concentrations decreased from 2010 to 2017 in four megacity clusters of eastern China, simulated for the month of January under fixed meteorological conditions (2010). Although the effectiveness further declined in 2020 for simulations including the natural experiment of substantial reductions in acid gas emissions during the COVID-19 pandemic, the resulting reductions in SIA concentrations were on average 20.8 % lower than those in 2017. In addition, the reduction in SIA concentrations in 2017 was greater for 50 % acid gas reductions than for the 50 % NH3 emission reductions. Our findings indicate that persistent secondary inorganic aerosol pollution in China is limited by emissions of acid gases, while an additional control of NH3 emissions would become more important as reductions of SO2 and NOx emissions progress.

Impacts of COVID-19 on Air Quality over China: Links with Meteorological Factors and Energy Consumption

The stringent control measures in China to curb the spread of Coronavirus disease (COVID-19) have had profound societal and environmental impacts, including changes in energy consumption practices and thereby in air pollutant emissions. In this study, a suite of satellite and numerically assimilated air pollution and meteorological data combined with information on energy consumption practices and nighttime light (NTL) was used to evaluate the effects of these COVID-19 control measures on air quality. These data revealed that control measures reduced aerosols mostly over central and eastern parts of China by countering favorable meteorological conditions for increased aerosols. The control measures reduced short-lived nitrogen dioxide (NO2) with little influence on long-lived carbon monoxide (CO). Consistent with energy production and energy consumption statistics in different sectors, NTL data suggest that high human mobility within the residential sector and reduced activity in other sectors during the implementation of control measures explain small but significant decreases in black carbon and sulfate aerosols, respectively, during this period. Overall, these results provide useful information for policy makers and the scientific community by clarifying the contributions of meteorological factors and energy consumption to changes in air quality. This information can guide the development of air pollution mitigation strategies and provides insight into the air pollution status in China and the potential for long-distance transport.

Air Quality Measurements in Kitchener, Ontario, Canada Using Multisensor Mini Monitoring Stations

The Region of Waterloo is the third fastest growing region in Southern Ontario in Canada with a population of 619,000 as of 2019. However, only one air quality monitoring station, located in a city park in Kitchener, Ontario, is currently being used to assess the air quality of the region. In September 2020, a network of AQMesh Multisensor Mini Monitoring Stations (pods) were installed near elementary schools in Kitchener located near different types of emission source. Data analysis using a custom-made long-distance scaling software showed that the levels of nitrogen oxides (NO and NO2), ground level ozone (O3), and fine particulate matter (PM2.5) were traffic related. These pollutants were used to calculate the Air Quality Health Index-Plus (AQHI+) at each location, highlighting the inability of the provincial air quality monitoring station to detect hotspot areas in the city. The case study presented here quantified the impact of the 2021 summer wildfires on the local air quality at a high time resolution (15-min). The findings in this article show that these multisensor pods are a viable alternative to expensive research-grade equipment. The results highlight the need for networks of local scale air quality measurements, particularly in fast-growing cities in Canada.