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

Correlation Between Daily PM10, Nitrogen Dioxide, and Ozone Measurements with the Stringency Index in 15 Different Districts of a Big Metropolis

OBJECTIVE

Studies have investigated the effects of lockdowns on air quality around the world and found that fine particulate matter and nitrogen dioxide concentrations decreased due to reduced human activity, while ozone concentrations increased. In this study, we aimed to evaluate the correlation between daily stringency index values of our country and daily PM10, nitrogen dioxide, and ozone measurements in different districts of Istanbul between March 1, 2020, and February 28, 2022.

MATERIAL AND METHODS

Ministry of Environment and Urbanization and National Air Quality Monitoring Network data on Istanbul air quality monitoring stations were used. The analysis included 15 stations that can monitor at least 75% of the days in a year. PM10, nitrogen dioxide, and ozone were the main pollutants analyzed.

RESULTS

There was negative correlation between daily PM10 measurements and daily stringency index values in 3 stations; there was positive correlation in 6 stations. Between daily stringency index values and daily nitrogen dioxide measurements, there was a negative correlation in 3 stations and a positive correlation in 1 station. The daily measurements of 1 station showed a negative correlation with the daily values of stringency index for both PM10 and nitrogen dioxide. In 1 station, while PM10 measures were negatively correlated with stringency index, nitrogen dioxide measurements were positively correlated.

CONCLUSION

This study showed that pandemic limitations could not improve Istanbul's air quality everywhere. For adequate evaluation of impact of the limitations on air quality, it may be more relevant to study the socioeconomic infrastructure of each living area, the sociospatial inequality, industrial employment, the number of households, the density of employee class, and so on with all influencing factors that could have contributed to these various changes.

The Effects of COVID-19 Lockdown on Air Pollutant Concentrations across China: A Google Earth Engine-Based Analysis

To overcome the spread of the severe COVID-19 outbreak, various lockdown measures have been taken worldwide. China imposed the strictest home-quarantine measures during the COVID-19 outbreak in the year 2020. This provides a valuable opportunity to study the impact of anthropogenic emission reductions on air quality. Based on the GEE platform and satellite imagery, this study analyzed the changes in the concentrations of NO₂, O₃, CO, and SO₂ in the same season (1 February-1 May) before and after the epidemic control (2019-2021) for 16 typical representative cities of China. The results showed that NO₂ concentrations significantly decreased by around 20-24% for different types of metropolises, whereas O₃ increased for most of the studied metropolises, including approximately 7% in megacities and other major cities. Additionally, the concentrations of CO and SO₂ showed no statistically significant changes during the study intervals. The study also indicated strong variations in air pollutants among different geographic regions. In addition to the methods in this study, it is essential to include the differences in meteorological impact factors in the study to identify future references for air pollution reduction measures.

Understanding Air Quality Changes after Implementation of Mitigation Measures during a Pandemic: A Scoping Review of Literature in the United States

Traffic-related emissions continue to be a significant source of air pollution in the United States (US) and around the globe. Evidence has shown that previous policies implemented to restrict-traffic flows have affected air pollution levels. Thus, mitigation strategies associated with the COVID-19 pandemic that modified population-level mobility patterns provide a unique opportunity to study air pollution change across the US. For instance, to slow the spread of the pandemic, state and local governments started implementing various mitigation actions, including stay-at-home directives, social distancing measures, school closures, and travel restrictions. This scoping review aimed to summarize the existing evidence about how air quality changed through mitigation practices throughout the pandemic in the US. We found 66 articles that fit our inclusion criteria. Generally, the consolidated results revealed that nitrogen dioxide (NO2) and carbon monoxide (CO) decreased across the country. Studies observed mixed directions and magnitudes of change for fine and coarse particulate matter (PM2.5, PM10), ozone (O3), and sulfur dioxide (SO2). Few articles tried to explain this notable heterogeneity in air quality changes by associating contextual factors, such as mobility, traffic flow, and demographic factors. However, all studies agreed that the change in air pollution was nonuniform across the US and even varied within a city.

The impact of COVID-19 lockdown on air pollution in Europe and North America: a systematic review

BACKGROUND

Multiple studies report reductions in air pollution associated with COVID-19 lockdowns.

METHODS

We performed a systematic review of the changes observed in hazardous air pollutants known or suspected to be harmful to health, including nitrogen dioxide (NO2), nitrogen oxides (NOx), carbon monoxide (CO), sulfur dioxide (SO2), ozone (O3) and particulate matter (PM). We searched PubMed and Web of Science for studies reporting the associations of lockdowns with air pollutant changes during the COVID-19 pandemic in Europe and North America.

RESULTS

One hundred nine studies were identified and analyzed. Several pollutants exhibited marked and sustained reductions. The strongest was NO2 (93% of 89 estimated changes were reductions) followed by CO (88% of 33 estimated pollutant changes). All NOx and benzene studies reported significant reductions although these were based on fewer than 10 estimates. About three-quarters of PM2.5 and PM10 estimates showed reductions and few studies reported increases when domestic fuel use rose during COVID-19 lockdowns. In contrast, O3 levels rose as NOx levels fell. SO2 and ammonia (NH3) had mixed results. In general, greater reductions appeared when lockdowns were more severe, as well as where baseline pollutant levels were higher, such as at low-elevation and in densely populated areas. Substantial and robust reductions in NO2, NO, CO, CO2, PM2.5, PM10, benzene and air quality index pollution occurred in association with COVID-19 lockdowns. O3 levels tended to increase, while SO2 and NH3 had mixed patterns.

CONCLUSIONS

Our study shows the profound impact of human activity levels on air pollution and its potential avoidability.

Analysis of Spatial and Temporal Changes of AQI in Wuhan City under the Urban Blockade of COVID-19 Pandemic

Wuhan was the primary city in the world to adopt lockdown measures during the Coronavirus Disease 2019 pandemic. The influence of the abrupt halt of human activities on the air quality of Wuhan is a subject of considerable attention. This study is based on air quality data from 21 monitoring stations in Wuhan from 2016 to 2020. The lag effect and seasonal factors of AQI were taken into account to analyze the changes in air quality in Wuhan under the influence of the pandemic blockade. The results showed the following during the city closure: (1) A lagging effect is observed in air quality changes, with the change point occurring on the 14th day after the city closure; (2) the air quality index is substantially decreased, demonstrating a reduction in spatial differences; (3) NO2, PM10, and PM2.5 significantly decreased whilst O3 increased, and SO2 and CO did not change significantly; (4) except for the insignificant changes in spatial differences of PM10, all pollutants demonstrated a changing pattern of decreasing geographical differences. This paper provides a reference for studying the influence of human activities on the natural environment.

The breathing human infrastructure: Integrating air quality, traffic, and social media indicators

Outdoor air pollution is a complex system that is responsible for the deaths of millions of people annually, yet the integration of interdisciplinary data necessary to assess air quality's multiple metrics is still lacking. This case study integrates atmospheric indicators (concentrations of criteria pollutants including particulate matter and gaseous pollutants), traffic indicators (permanent traffic monitoring station data), and social indicators (community responses in Twitter archives) representing the interplay of the three critical pillars of the United Nations' Triple Bottom Line: environment, economy, and society. During the watershed moment of the COVID-19 pandemic lockdowns in Florida, urban centers demonstrated the gaps and opportunities for understanding the relationships, through correlations rather than causations, between urban air quality, traffic emissions, and public perceptions. The relationship between the perception and the traffic variables were strongly correlated, however no correlation was observed between the perception and actual air quality indicators, except for NO₂. These observations might consequently infer that traffic serves as people's proxy for air quality, regardless of actual air quality, suggesting that social media messaging around asthma may be a way to monitor traffic patterns in areas where no infrastructure currently exists or is prohibited to build. It also indicates that people are less likely to be reliable sensors to accurately measure air quality due to bias in their observations of traffic volume and/or confirmation biases in broader social discourse. Results presented herein are of significance in demonstrating the capacity for interdisciplinary studies to consider the predictive capacities of social media and air pollution, its use as both lever and indicator of public support for air quality legislation and clean-air transitions, and its ability to overcome limitations of surface monitoring stations.

Future assessment of the impact of the COVID-19 pandemic on the electricity market based on a stochastic socioeconomic model

This paper proposes a time-series stochastic socioeconomic model for analyzing the impact of the pandemic on the regulated distribution electricity market. The proposed methodology combines the optimized tariff model (socioeconomic market model) and the random walk concept (risk assessment technique) to ensure robustness/accuracy. The model enables both a past and future analysis of the impact of the pandemic, which is essential to prepare regulatory agencies beforehand and allow enough time for the development of efficient public policies. By applying it to six Brazilian concession areas, results demonstrate that consumers have been/will be heavily affected in general, mainly due to the high electricity tariffs that took place with the pandemic, overcoming the natural trend of the market. In contrast, the model demonstrates that the pandemic did not/will not significantly harm power distribution companies in general, mainly due to the loan granted by the regulator agency, named COVID-account. Socioeconomic welfare losses averaging 500 (MR$/month) are estimated for the equivalent concession area, i.e., the sum of the six analyzed concession areas. Furthermore, this paper proposes a stochastic optimization problem to mitigate the impact of the pandemic on the electricity market over time, considering the interests of consumers, power distribution companies, and the government. Results demonstrate that it is successful as the tariffs provided by the algorithm compensate for the reduction in demand while increasing the socioeconomic welfare of the market.

Effects of the COVID-19 lockdown and recovery on People's mobility and air quality in the United Arab Emirates using satellite and ground observations

The stringent COVID-19 lockdown measures in 2020 significantly impacted people's mobility and air quality worldwide. This study presents an assessment of the impacts of the lockdown and the subsequent reopening on air quality and people's mobility in the United Arab Emirates (UAE). Google's community mobility reports and UAE's government lockdown measures were used to assess the changes in the mobility patterns. Time-series and statistical analyses of various air pollutants levels (NO₂, O₃, SO₂, PM₁₀, and aerosol optical depth-AOD) obtained from satellite images and ground monitoring stations were used to assess air quality. The levels of pollutants during the initial lockdown (March to June 2020) and the subsequent gradual reopening in 2020 and 2021 were compared with their average levels during 2015-2019. During the lockdown, people's mobility in the workplace, parks, shops and pharmacies, transit stations, and retail and recreation sectors decreased by about 34%-79%. However, the mobility in the residential sector increased by up to 29%. The satellite-based data indicated significant reductions in NO₂ (up to 22%), SO₂ (up to 17%), and AOD (up to 40%) with small changes in O₃ (up to 5%) during the lockdown. Similarly, data from the ground monitoring stations showed significant reductions in NO₂ (49% - 57%) and PM₁₀ (19% - 64%); however, the SO₂ and O₃ levels showed inconsistent trends. The ground and satellite-based air quality levels were positively correlated for NO₂, PM₁₀, and AOD. The data also demonstrated significant correlations between the mobility and NO₂ and AOD levels during the lockdown and recovery periods. The study documents the impacts of the lockdown on people's mobility and air quality and provides useful data and analyses for researchers, planners, and policymakers relevant to managing risk, mobility, and air quality.

Designing Post COVID-19 Buildings: Approaches for Achieving Healthy Buildings

The COVID-19 pandemic forced the accessibility, social gathering, lifestyle, and working environment to be changed to reduce the infection. Coronavirus spreads between people in several different ways. Small liquid particles (aerosols, respiratory droplets) from an infected person are transmitted through air and surfaces that are in contact with humans. Reducing transmission through modified heating, ventilation, and air conditioning (HVAC) systems and building design are potential solutions. A comprehensive review of the engineering control preventive measures to mitigate COVID-19 spread, healthy building design, and material was carried out. The current state-of-the-art engineering control preventive measures presented include ultraviolet germicidal irradiation (UVGI), bipolar ionization, vertical gardening, and indoor plants. They have potential to improve the indoor air quality. In addition, this article presents building design with materials (e.g., copper alloys, anti-microbial paintings) and smart technologies (e.g., automation, voice control, and artificial intelligence-based facial recognition) to mitigate the infections of communicable diseases.

Modelling the effect of local and regional emissions on PM2.5 concentrations in Wuhan, China during the COVID-19 lockdown

PM_2.5 concentrations in Wuhan, China decreased by 36.0% between the period prior to the COVID-19 pandemic (1–23 January, 2020) and the COVID-lockdown period (24 January to 29 February, 2020). However, decreases in PM_2.5 concentration due to regional PM_2.5 transport driven by meteorological changes, and the relationship between the PM_2.5 source and receptor, are poorly understood. Therefore, this study assessed how changes in meteorology, local emissions, and regional transport from external source emissions contributed to the decrease in Wuhan's PM_2.5 concentration, using FLEXPART-WRF and WRF-Chem modelling experiments. The results showed that meteorological changes in central China explain up to 22.2% of the total decrease in PM_2.5 concentrations in Wuhan, while the remaining 77.8% was due to air pollutant emissions reduction. Reduction in air pollutant emissions depended on both local and external sources, which contributed alomst equally to the reduction in PM_2.5 concentrations (38.7% and 39.1% of the total reduction, respectively). The key emissions source areas affecting PM_2.5 in Wuhan during the COVID-lockdown were identified by the FLEXPART-WRF modeling, revealing that regional-joint control measures in key areas accounted for 89.3% of the decrease in PM_2.5 concentrations in Wuhan. The results show that regional-joint control can be enhanced by identifying key areas of emissions reduction from the source–receptor relationship of regional PM_2.5 transport driven by meteorology under the background of East Asian monsoon climate change.