Impact of the COVID-19 Pandemic on Air Quality in Metropolitan New Jersey.

Improved air quality has been the silver lining of the pandemic since early 2020. The air quality in northern New Jersey (NJ) was continuously measured during the COVID-19 pandemic and through the three stages of recovery, i.e., the Stay-at-home stage, Reopening stage 1, and Reopening stage 2. A significant change in air quality was observed during the Stay-at-home stage (March 16 to May 16, 2020) as most people stayed home and industrial activity decreased 60%. Compared to 2019, carbon dioxide (CO2) decreased 17%, carbon monoxide (CO) decreased 7%, and nitrogen oxides (NOx) decreased 51% during the Stay-at-home stage in 2020. However, the ground-level ozone (O3) increased in 2020 because of the reduced NOx emission and the possibly increased levels of volatile organic compounds (VOCs) due to warmer weather. With the step-by-step reopening process, the difference in local CO2 levels between 2019 and 2020 was reduced, and the NOx concentration returned to its 2019 level. The CO2 concentrations were positively correlated with CO, and the NOx concentrations were negatively correlated with O3. Under the COVID-19 pandemic in 2020, NJ consumed 14% less natural gas and 21% less gasoline; therefore, the CO2, CO, and NOx emissions and concentration levels were reduced besides the effects of meteorology parameters on air quality in metropolitan New Jersey. Our findings support that replacing fossil fuels with electric or renewable energy in the transportation systems and industry could be beneficial for the concentration reduction of certain greenhouse gases. Supplementary Information: The online version contains supplementary material available at 10.1007/s11270-022-05764-w.

Predicting ozone formation in petrochemical industrialized Lanzhou city by interpretable ensemble machine learning.

Ground-level ozone (O3) formation depends on meteorology, precursor emissions, and atmospheric chemistry. Understanding the key drivers behind the O3 formation and developing an accurate and efficient method for timely assessing the O3-VOCs-NOx relationships applicable in different O3 pollution events are essential. Here, we developed a novel machine learning ensemble model coupled with a Shapley additive explanation algorithm to predict the O3 formation regime and derive O3 formation sensitivity curves. The algorithm was tested for O3 events during the COVID-19 lockdown, a sandstorm event, and a heavy O3 pollution episode (maximum hourly O3 concentration >200 µg/m3) from 2019 to 2021. We show that increasing O3 concentrations during the COVID-19 lockdown and the heavy O3 pollution event were mainly caused by the photochemistry subject to local air quality and meteorological conditions. Influenced by the sandstorm weather, low O3 levels were mainly attributable to weak sunlight and low precursor levels. O3 formation sensitivity curves demonstrate that O3 formation in the study area was in a VOCs-sensitive regime. The VOCs-specific O3 sensitivity curves can also help make hybrid and timely strategies for O3 abatement. The results demonstrate that machine learning driven by observational data has the potential to be a very useful tool in predicting and interpreting O3 formation.

<span contenteditable="false" data-cke-magic-line="1" style="width: 695px;height: 0px;padding: 0px;margin: 0px;display: block;z-index: 9999;color: rgb(255,255,255);font-size: 0px;line-height: 0px;position: absolute;border-top: 1px dashed rgb(255,0,0);user-select: none;left:-2px;top: 64.2px;"><span style="color:#ffffff"> </span><span style="color:#ffffff"> </span><span style="color:#ffffff">?</span></span>Estimating daily ground-level NO2 concentrations over China based on TROPOMI observations and machine learning approach

Nitrogen dioxide (NO2) is an important target for monitoring atmospheric quality. Deriving ground-level NO2 concentrations with much finer resolution, it requires high-resolution satellite tropospheric NO2 column as input and a reliable estimation algorithm. This paper aims to estimate the daily ground-level NO2 concentrations over China based on machine learning models and the TROPOMI NO2 data with high spatial resolution. In this study, four tree-based algorithm machine learning models, decision trees (DT), gradient boost decision tree (GBDT), random forest (RF) and extra-trees (ET), were used to estimate ground-level NO2 concentrations. In addition to considering many influencing factors of the ground-level NO2 concentrations, we especially introduced simplified temporal and spatial information into the estimation models. The results show that the extra-trees with spatial and temporal information (ST-ET) model has great performance in estimating ground-level NO2 concentrations with a cross-validation R-2 of 0.81 and RMSE of 3.45 mu g/m(3) in test datasets. The estimated results for 2019 based on the ST-ET model achieves a satisfactory accuracy with a cross-validation R-2 of 0.86 compared with the other models. Through time-space analysis and comparison, it was found that the estimated high-resolution results were consistent with the ground observed NO2 concentrations. Using data from January 2020 to test the prediction power of the models, the results indicate that the ST-ET model has a good performance in predicting ground-level NO2 concentrations. Taking four ground-level NO2 concentrations hotspots as examples, the estimated ground-level NO2 concentrations and ground-based observation data during the coronavirus disease (COVID-19) pandemic were lower compared with the same period in 2019. The findings offer a solid solution for accurately and efficiently estimating ground-level NO2 concentrations by using satellite observations, and provide useful information for improving our understanding of the regional atmospheric environment.

Estimating daily ground-level NO2 concentrations over China based on TROPOMI observations and machine learning approach

Nitrogen dioxide (NO2) is an important target for monitoring atmospheric quality. Deriving ground-level NO2 concentrations with much finer resolution, it requires high-resolution satellite tropospheric NO2 column as input and a reliable estimation algorithm. This paper aims to estimate the daily ground-level NO2 concentrations over China based on machine learning models and the TROPOMI NO2 data with high spatial resolution. In this study, four tree-based algorithm machine learning models, decision trees (DT), gradient boost decision tree (GBDT), random forest (RF) and extra-trees (ET), were used to estimate ground-level NO2 concentrations. In addition to considering many influencing factors of the ground-level NO2 concentrations, we especially introduced simplified temporal and spatial information into the estimation models. The results show that the extra-trees with spatial and temporal information (ST-ET) model has great performance in estimating ground-level NO2 concentrations with a cross-validation R2 of 0.81 and RMSE of 3.45 μg/m3 in test datasets. The estimated results for 2019 based on the ST-ET model achieves a satisfactory accuracy with a cross-validation R2 of 0.86 compared with the other models. Through time-space analysis and comparison, it was found that the estimated high-resolution results were consistent with the ground observed NO2 concentrations. Using data from January 2020 to test the prediction power of the models, the results indicate that the ST-ET model has a good performance in predicting ground-level NO2 concentrations. Taking four ground-level NO2 concentrations hotspots as examples, the estimated ground-level NO2 concentrations and ground-based observation data during the coronavirus disease (COVID-19) pandemic were lower compared with the same period in 2019. The findings offer a solid solution for accurately and efficiently estimating ground-level NO2 concentrations by using satellite observations, and provide useful information for improving our understanding of the regional atmospheric environment.

Strategy Based on Two Stages for IR Thermographic Inspections of Photovoltaic Plants

Aerial infrared (IR) thermography has been implemented in recent years, proving to be a powerful and versatile technique for performing maintenance at photovoltaic (PV) plants. Its application speed and reliability using unmanned aerial vehicles (UAVs) or drones make it extremely interesting at large PV plants, due to the associated savings in time and costs. Ground-level thermographic inspection is slower and more costly to apply, although it does provide higher optical resolution, due to being conducted closer to the PV modules being inspected. Both techniques used in combination can improve the diagnosis. An IR thermography inspection strategy is proposed for PV plants based on two stages. The first stage of the inspection is aerial, enabling thermal faults to be detected and located quickly and reliably. The second stage of the inspection is done on the ground and applied only to the most relevant incidents revealed in the first stage. This inspection strategy was applied to a 100 kW PV plant, with an improved diagnosis verified via this procedure, as the ground-level inspection detects one-off thermal incidents from objects creating shade and from solar reflections. For PV modules with open circuits or open substrings, the use of one technique or another is immaterial.

Using the Halophyte Crithmum maritimum in Green Roofs for Sustainable Urban Horticulture: Effect of Substrate and Nutrient Content Analysis including Potentially Toxic Elements

The effect of substrate type and cultivation site in the urban fabric on growth, nutrient content and potentially toxic element (PTE) accumulation in tissues of the halophyte Crithmum maritimum was studied. Plantlets were cultivated for twelve months in containers with a green-roof infrastructure fitted and placed either on an urban second-floor roof or on ground level by the side of a moderate-traffic street. Two substrate types were used;one comprising grape marc compost, perlite and pumice (3:3:4, v/v) and one composed of grape marc compost, perlite, pumice and soil (3:3:2:2, v/v), with 10 cm depth. Plants grew well on both sites, although aboveground growth parameters and nutrient content in leaves were greater at street level. Both cultivation site and substrate type affected heavy-metal accumulation in plant tissues. Cu, Ni and Fe concentrations in leaves and Pb in roots were higher in street-level-grown plants compared to the roof-grown plants, and concentrations of Cu and Mn in leaves and Fe in both leaves and roots were lower in the soilless substrate compared to the soil-substrate, making the soilless type preferable in the interest of both safer produce for human consumption and lower construction weight in the case of green-roof cultivation.

COVID-19 Lockdown: Lessons learnt using multiple air quality monitoring station data from Kolkata city in India

On March 16, 2020, Kolkata megacity in India announced partial lockdown due to COVID-19 crisis. This study presents an analysis for multiple pollutants with special focus on NO2 and O3 based on data from different monitoring stations located to across Kolkata city for the period from 16 March-17 May 2020 compared to the pre-lockdown period. Most significant reduction was observed in the concentration of nitrogen dioxide (NO2) (- 76.8%), volatile organic compounds (VOCs) (- 69.5%), PM10 (- 64.6%) and PM2.5 (- 60.9%). A lower reduction percentage was found for CO, sulfur dioxide (SO2) and ammonia (-48.6%, - 41.7% and – 41.1% respectively). However, during partial lockdown, lockdown phase-1, phase-2 and phase-3, surface-level ozone (O3) has changed respectively by 31.72%, 31.13%, -14.28% and -14.05% which resulted in an overall increase of 8.17% in the entire study period. The air quality index (AQI) in Kolkata which usually remains poor or very poor, even during lockdown period, failed to attain the ‘good’ standard. This needs special attention in human health impact assessment and public health management. We recommend additional attention to be drawn towards stickiness in O3 which had adverse human health and which went up during lockdown period compared to pre-lockdown period. We highlight some major policy implications of the observed trends to combat city air pollution along with climate co-benefits by shifting transport fuel and related infrastructure. © 2022 World Research Association. All rights reserved.

Insights on In-Situ Photochemistry Associated with Ozone Reduction in Guangzhou during the COVID-19 Lockdown

Increases in ground-level ozone (O3 ) have been observed during the COVID-19 lockdown in many places around the world, primarily due to the uncoordinated emission reductions of O3 precursors. In Guangzhou, the capital of Guangdong province in South China, O3 distinctively decreased during the lockdown. Such a phenomenon was attributed to meteorological variations and weakening of local O3 formation, as indicated by chemical transport models. However, the emission-based modellings were not fully validated by observations, especially for volatile organic compounds (VOCs). In this study, we analyzed the changes of O3 and its precursors, including VOCs, from the pre-lockdown (Pre-LD) to lockdown period (LD) spanning 1 week in Guangzhou. An observation-based box model was applied to understand the evolution of in-situ photochemistry. Indeed, the ambient concentrations of O3 precursors decreased significantly in the LD. A reduction of 20.7% was identified for the total mixing ratios of VOCs, and the transportation-related species experienced the biggest declines. However, the reduction of O3 precursors would not lead to a decrease of in-situ O3 production if the meteorology did not change between the Pre-LD and LD periods. Sensitivity tests indicated that O3 formation was limited by VOCs in both periods. The lower temperature and photolysis frequencies in the LD reversed the increase of O3 that would be caused by the emission reductions otherwise. This study reiterates the fact that O3 abatement requires coordinated control strategies, even if the emissions of O3 precursors can be significantly reduced in the short term. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.

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.

Comparative assessment of modeling deep learning networks for modeling ground-level ozone concentrations of pandemic lock-down period.

Covid-19 pandemic lock-down has resulted significant differences in air quality levels all over the world. In contrary to decrease seen in primary pollutant species, many of the countries have experienced elevated ground-level ozone levels in this period. Air pollution forecast gains more importance to achieve air quality management and take measures against the risks under such extra-ordinary conditions. Statistical models are indispensable tools for predicting air pollution levels. Considering the complex photochemical reactions involved in tropospheric ozone formation, modeling this pollutant requires efficient non-linear approaches. In this study, deep learning methods were applied to forecast hourly ozone levels during pandemic lock-down for an industrialized region in Turkey. With this aim, different deep learning methods were tested and efficiencies of the models were compared considering the calculated RMSE, MAE, R 2 and loss values.

Effect of COVID-19 pandemic on air quality: a study based on Air Quality Index.

The COVID-19 pandemic has significantly affected economic activities all around the world. Though it took a huge amount of human breathes as well as increases unemployment, it puts a positive impression on the environment. To stop the speedy extend of this disease, the maximum Government has imposed a strict lockdown on their citizens which creates a constructive impact on the atmosphere. Air pollutant concentration has been investigated in this study to analyze the impact of lockdown on the environment. Based on the air pollutant concentration, Air Quality Index (AQI) is deliberated. The Air Quality Index indicates the most and least polluted cities in the world. A higher value of AQI represents the higher polluted city and a lesser value of Air Quality Index represents a less polluted city. The impact of lockdown on air quality has been studied in this work and it is observed that the air pollutant concentration has reduced in every city of the world during the lockdown period. It has been also detected that the PM2.5 and PM10 are the most affecting air concentrator which controls the air quality of all the selected places during and after lockdown.

On the impact of the COVID-19 pandemic on air quality in Florida.

Since early 2020, the world has faced an unprecedented pandemic caused by the novel COVID-19 virus. In this study, we characterize the impact of the lockdown associated with the pandemic on air quality in six major cities across the state of Florida, namely: Jacksonville, Tallahassee, Gainesville, Orlando, Tampa, and Miami. Hourly measurements of PM2.5, ozone, NO2, SO2, and CO were provided by the US EPA at thirty sites operated by the Florida Department of Environmental Protection during mid-February to mid-April from 2015 through 2020. To analyze the effect of the pandemic, atmospheric pollutant concentrations in 2020 were compared to historic data at these cities during the same period from 2015 to 2019. Reductions in NO2 and CO levels were observed across the state in most cities and were attributed to restrictions in mobility and the decrease in vehicle usage amid the lockdown. Likewise, decreases in O3 concentrations were observed and were related to the prevailing NOx-limited regime during this time period. Changes in concentrations of SO2 exhibited spatial variations, concentrations decreased in northern cities, however an increase was observed in central and southern cities, likely due to increased power generation at facilities primarily in the central and southern regions of the state. PM2.5 levels varied temporally during the study and were positively correlated with SO2 concentrations during the lockdown. In March, reductions in PM2.5 levels were observed, however elevations in PM2.5 concentrations in April were attributed to long-range transport of pollutants rather than local emissions. This study provides further insight into the impacts of the COVID-19 pandemic on anthropogenic sources from vehicular emissions and power generation in Florida. This work has implications for policies and regulations of vehicular emissions as well as consequences on the use of sustainable energy sources in the state.

Global, continental, and national variation in PM2.5, O3, and NO2 concentrations during the early 2020 COVID-19 lockdown.

Lockdowns implemented in response to COVID-19 have caused an unprecedented reduction in global economic and transport activity. In this study, variation in the concentration of health-threatening air pollutants (PM2.5, NO2, and O3) pre- and post-lockdown was investigated at global, continental, and national scales. We analyzed ground-based data from >10,000 monitoring stations in 380 cities across the globe. Global-scale results during lockdown (March to May 2020) showed that concentrations of PM2.5 and NO2 decreased by 16.1% and 45.8%, respectively, compared to the baseline period (2015-2019). However, O3 concentration increased by 5.4%. At the continental scale, concentrations of PM2.5 and NO2 substantially dropped in 2020 across all continents during lockdown compared to the baseline, with a maximum reduction of 20.4% for PM2.5 in East Asia and 42.5% for NO2 in Europe. The maximum reduction in O3 was observed in North America (7.8%), followed by Asia (0.7%), while small increases were found in other continents. At the national scale, PM2.5 and NO2 concentrations decreased significantly during lockdown, but O3 concentration showed varying patterns among countries. We found maximum reductions of 50.8% for PM2.5 in India and 103.5% for NO2 in Spain. The maximum reduction in O3 (22.5%) was found in India. Improvements in air quality were temporary as pollution levels increased in cities since lockdowns were lifted. We posit that these unprecedented changes in air pollutants were mainly attributable to reductions in traffic and industrial activities. Column reductions could also be explained by meteorological variability and a decline in emissions caused by environmental policy regulations. Our results have implications for the continued implementation of strict air quality policies and emission control strategies to improve environmental and human health.