Getting to the STEM of Environmental Justice: Equity Causes Enrich Student Learning in Science Classrooms

From satellites to ground-based sensors, as well as mobile networks of monitors, the availability of massive data sets has increased the need for educating students in data literacy in order to ensure their competency in the global market (Bluhm et al. 2020;Gibson and Mourad 2018). The U.S. Environmental Protection Agency (EPA) defines environmental justice as, "... the fair treatment and meaningful involvement of all people regardless of race, color, national origin, or income, with respect to the development, implementation, and enforcement of environmental laws, regulations, and policies." According to Lacombe, more individuals die yearly from car exhaust (53,000) in the United States than road casualties (37,400). Students worked in groups to discuss their assumptions about factors they perceived to have an impact on air pollution levels (e.g., affluence, traffic, and vegetation).

Effect of COVID-19 pandemic restrictions on air pollution at a local scale in urban areas affected by high-intensity vehicle traffic in Poland

The lockdown in 2020 implemented due to the SARS-CoV-2 pandemic has resulted in a significant improvement in air quality at a global scale. Nationwide lockdown also considerably improved air quality at a local scale, especially in cities which were almost completely shut down during the first coronavirus wave, with nearly no activity. We tested the hypothesis that a reduction in the intensity of vehicle traffic causes a drastic decrease in urban air pollution at a local scale. We focused on two urban agglomerations, Warsaw and Cracow, in Poland. Data of the concentrations of traffic-related sources, namely NOx, PM10, and PM2.5, obtained from two air pollution monitoring stations were analyzed for the years 2020 and 2021, during which lockdown and pandemic restrictions were in effect, and for 2019, as a reference. In the years 2020–2021, the average annual concentration of NOx was decreased by ~ 19%, PM2.5 by ~ 19%, and PM10 by ~ 18% in Warsaw, while in Cracow the average annual concentration of NOx was decreased by ~ 16%, PM2.5 by ~ 22%, and PM10 by ~ 2%, compared to 2019. The contribution from traffic-related sources to the overall level of air pollution was estimated. The results indicated that ~ 30 µg/m3 of PM10, ~ 15 µg/m3 of PM2.5, and ~ 120 µg/m3 of NOx in Cracow, and ~ 20 µg/m3 of PM2.5 in Warsaw originate from moving vehicles. The nationwide lockdown allowed us to conduct this study to understand how a reduction in local traffic emissions can decrease ambient air pollution levels.

Positive Matrix Factorization as Source Apportionment of Paddy Soil Heavy Metals in Black Shale Areas in Western Zhejiang Province, China

The source apportionment of pollutants is the key to preventing and controlling the pollution caused by heavy metals in soils. The aim of this study was to investigate the main sources of heavy metals in the soils of black shale areas in western Zhejiang, China. Based on geostatistical spatial analysis, this research employed positive matrix factorization (PMF) for the source apportionment of heavy metals in paddy soil. The results showed that contaminated arable soils were concentrated in the western and southern study areas. At least five major sources of heavy metals were screened in this study: natural sources (39.66%), traffic emissions (32.85%), industrial emissions (9.23%), agricultural activities (9.17%), and mining (9.10%). To be specific, Cd was mainly from mining;As originated from agricultural inputs such as fertilizers and pesticides;and Hg, as an industrial pollutant, was transported by atmospheric deposition in the study area. The accumulation of Pb, Zn, and Cu was mainly influenced by natural sources and anthropogenic sources, i.e., traffic emissions, while that of Cr and Ni was controlled by natural sources.

Promoting economic and environmental resilience in the post-COVID-19 era through the city and regional on-road fuel sustainability development

How to control the global temperature rise within 1.5 °C in the post-COVID-19 era has attracted attention. Road transport accounts for nearly a quarter of global CO2 emissions, and the related sulfur dioxide (SO2) emissions also trigger air pollution issues in population-intensive cities and areas. Many cities and states have announced a timetable for phasing out urban-based fossil fuel vehicles. By combining a Markov-chain model with a dynamic stochastic general equilibrium (DSGE) model, the impacts of on-road energy structural change led by phasing out fossil fuel vehicles in the road transportation sector are evaluated. The impact of automobile emissions (both CO2 and SO2) on the environment is evaluated, taking into consideration of variation between cities, regions, and countries. Two other major driving forces in addition to CO2 emissions reduction in promoting fossil fuel vehicles' transition toward net-zero carbon are identified and analyzed with multiple different indicators. Under the framework of the DSGE model, climate policy instruments' effects on economic development, energy consumption, and their link to economic and environmental resilience are evaluated under exogenous shocks as well.

Bio-monitoring of atmospheric heavy metal pollution of major emergency events using moss bags:a case study in Xichang

In order to trace and monitor the atmospheric heavy metal pollution in Xichang City, an investigation activity was carried out with a sort of moss (Taxiphyllum taxirameum) (packed in moss bags) as a biological indicator for monitoring heavy metal pollution. The investigation was conducted from the period from April 2019 to April 2020, during which two grave emergency events occurred during spring monitoring period from January 15 to April 15, 2020, i.e., COVID-19 and "3.30"severe forest fire in Xichang, which inevitably affected the atmospheric quality. Based on the concentration analysis of 12 kinds of heavy metal, including Al, Cr, Fe, Cu, Ni, Pb, Mn, Hg, Zn, V, As and Ba contained in the moss and the local meteorological data, comparing those informative data before and after the time when the emergency events toke place, the paper made an analysis on the impacts of two enormous emergency events on the air pollution of heavy metal in Xichang. The results showed that total amount of enrichment of above-mentioned 12 heavy metals in spring (January 15 to April 15, 2020) is (12.85 +or- 1.57) mg/g, which was significantly higher than in the other three seasons (p < 0.01), but no significant discrepancies about the total enrichment amount in the other three seasons (p > 0.05). Primarily because of COVID-19 pandemic, the level of motor vehicles emissions cut down, and the decrease of the tourism in the related areas perhaps causing the decline of pollution of Pb. In addition, the decrease of unbalanced emission of pollutants led to a noted increase of atmospheric oxidation in urban area, thus boosting the formation of secondary particulate matter, and the particulate matter from surrounding industrial sources was transported into the urban area;as a result, remarkable increases of Hg concentration of moss within the moss bags were detected downwind the industrial area located in the urban fringe. Consequently, the investigation showed that the moss-bag method is an effective biological tool for monitoring air heavy metal pollution, which could reflect the impacts of major pollution events on air quality.

The effect of COVID-19 restrictions on atmospheric new particle formation in Beijing

During the COVID-19 lockdown, the dramatic reduction of anthropogenic emissions provided a unique opportunity to investigate the effects of reduced anthropogenic activity and primary emissions on atmospheric chemical processes and the consequent formation of secondary pollutants. Here, we utilize comprehensive observations to examine the response of atmospheric new particle formation (NPF) to the changes in the atmospheric chemical cocktail. We find that the main clustering process was unaffected by the drastically reduced traffic emissions, and the formation rate of 1.5 nm particles remained unaltered. However, particle survival probability was enhanced due to an increased particle growth rate (GR) during the lockdown period, explaining the enhanced NPF activity in earlier studies. For GR at 1.5–3 nm, sulfuric acid (SA) was the main contributor at high temperatures, whilst there were unaccounted contributing vapors at low temperatures. For GR at 3–7 and 7–15 nm, oxygenated organic molecules (OOMs) played a major role. Surprisingly, OOM composition and volatility were insensitive to the large change of atmospheric NOx concentration;instead the associated high particle growth rates and high OOM concentration during the lockdown period were mostly caused by the enhanced atmospheric oxidative capacity. Overall, our findings suggest a limited role of traffic emissions in NPF.

Chemical characteristics and sources of PM2.5 in Hohhot, a semi-arid city in northern China: insight from the COVID-19 lockdown

A knowledge gap exists concerning how chemical composition and sources respond to implemented policy control measures for aerosols, particularly in a semi-arid region. To address this, a single year's offline measurement was conducted in Hohhot, a semi-arid city in northern China, to reveal the driving factors of severe air pollution in a semi-arid region and assess the impact of the COVID-19 lockdown measures on chemical characteristics and sources of PM2.5. Organic matter, mineral dust, sulfate and nitrate accounted for 31.5 %, 14.2 %, 13.4 % and 12.3 % of the total PM2.5 mass, respectively. Coal combustion, vehicular emission, crustal source and secondary inorganic aerosols were the main sources of PM2.5 in Hohhot, at 38.3 %, 35.0 %, 13.5 %, and 11.4 %, respectively. Due to the coupling effect of emission reduction and improved atmospheric conditions, the concentration of secondary inorganic components, organic matter and elemental carbon declined substantially from the pre-lockdown (pre-LD) period to the lockdown (LD) and post-lockdown (post-LD) periods. The source contribution of secondary inorganic aerosols increased (from 21.1 % to 37.8 %), whereas the contribution of vehicular emission reduced (from 35.5 % to 4.4 %) due to lockdown measures. The rapid generation of secondary inorganic components caused by unfavorable meteorological conditions during lockdown led to serious pollution. This study elucidates the complex relationship between air quality and environmental policy.

Urban Air Pollution, Urban Heat Island and Human Health: A Review of the Literature

Many cities of the world suffer from air pollution because of poor planning and design and heavy traffic in rapidly expanding urban environments. These conditions are exacerbated due to the Urban Heat Island (UHI) effect. While there have been studies linking the built environment and air pollution with health, they have ignored the aggravating role of UHI. The past urban planning literature in this field has also ignored the science of materials, vehicles and air pollution, and technological solutions for reducing cumulative health impacts of air pollution and UHI. Air Pollution, built environment and human health are complex discussion factors that involve several different fields. The built environment is linked with human health through opportunities of physical activity and air quality. Recent planning literature focuses on creating compact and walkable urban areas dotted with green infrastructure to promote physical activity and to reduce vehicle emission-related air pollution. Reduced car use leading to reduced air pollution and UHI is implied in the literature. The literature from technology fields speaks to the issue of air pollution directly. Zero emission cars, green infrastructure and building materials that absorb air pollutants and reduce UHI fall within this category. This paper identifies main themes in the two streams of urban air pollution and UHI that impact human health and presents a systematic review of the academic papers, policy documents, reports and features in print media published in the last 10–20 years.

Environmental Geochemistry and Positive Impact of Covid-19 Pandemic with Special Emphasis to Coal Mining Industry: An Overview

In the present situation, Covid-19 is considered to be an unbeaten global pandemic. In every single fleeting moment, this SARS-CoV-2 (coronavirus-2) causes greater damage to our life including the physical world including drastic imbalance of the whole economic condition of any country. The lockdown governed in two consecutive years (2020 and 2021) in the world to control the spreading of the virus poses an undue threat to the industrial sectors including the coal mining sectors that determine the economic growth of the country. With these negative impacts of coronavirus-2 in our life, this present review aims to explore some of the positive influences of the Covid-19 pandemic through the restoration of the environmental system which are otherwise not possible. This quantitative review finds that spreading of the Covid-19 pandemic indirectly improves the air and water quality by reducing the number of vehicles, reduces the CO2, NOx, particulate matter, and other polluting gases emission from coal-based power plants through periodical lockdown in the country. Moreover, the lockdown implemented to minimise the spreading of the Covid-19 significantly reduces the coal dust production from the mining and transportation of coal that indirectly reduces environmental pollution.

Measurement report: The importance of biomass burning in light extinction and direct radiative effect of urban aerosol during the COVID-19 lockdown in Xi'an, China

Due to the complexity of emission sources, a better understanding of aerosol optical properties is required to mitigate climate change in China. Here, an intensive real-time measurement campaign was conducted in an urban area of China before and during the COVID-19 lockdown in order to explore the impacts of anthropogenic activities on aerosol light extinction and the direct radiative effect (DRE). The mean light extinction coefficient (bext) decreased from 774.7 ± 298.1 Mm-1 during the normal period to 544.3 ± 179.4 Mm-1 during the lockdown period. A generalised additive model analysis indicated that the large decline in bext (29.7 %) was due to sharp reductions in anthropogenic emissions. Chemical calculation of bext based on a ridge regression analysis showed that organic aerosol (OA) was the largest contributor to bext in both periods (45.1 %–61.4 %), and the contributions of two oxygenated OAs to bext increased by 3.0 %–14.6 % during the lockdown. A hybrid environmental receptor model combined with chemical and optical variables identified six sources of bext. It was found thatbext from traffic-related emissions, coal combustion, fugitive dust, the nitrate and secondary OA (SOA) source, and the sulfate and SOA source decreased by 21.4 %–97.9 % in the lockdown, whereas bext from biomass burning increased by 27.1 %, mainly driven by the undiminished need for residential cooking and heating. An atmospheric radiative transfer model was further used to illustrate that biomass burning, rather than traffic-related emissions, became the largest positive effect (10.0 ± 10.9 W m-2) on aerosol DRE in the atmosphere during the lockdown. Our study provides insights into aerosol bext and DRE from anthropogenic sources, and the results imply the importance of controlling biomass burning for tackling climate change in China in the future.

Variability of fine and inhalable particles in controlled and unbridled anthropogenic activities: from a perspective of lockdown containment measures in Visakhapatnam city

Rapid industrialization and urbanization contributed to deteriorating air quality via the pollutants generated through industrial and concomitant activities. Air quality has an impact on human health, especially respiratory and heart related diseases. The present study attempts to analyze the average concentrations of fine particulate matter of the sizes PM2.5 and PM10 in Visakhapatnam city of Andhra Pradesh state of India. The study infers that the PM2.5 and the PM10 are predominantly dominated by the increasing vehicular traffic and the pollutants released by industrial activities including port. The seasonal variations of PM2.5 and PM10 ratios showed the dominance of anthropogenic aerosols because of industrialization and heavy traffic in the city. The impact of lockdown as a preventive measure to contain COVID-19 during the period 25th March 3rd May 2020 improved air quality significantly which can be clearly seen in day and night time concentrations of PM2.5 and PM10 during summer season. The study finds that day and night time average concentrations of PM2.5 and PM10 are low in summer in comparison to the other seasons. The impact of lockdown as a preventive measure in COVID-19 pandemic period covering the period 25th March 3rd May 2020 is conspious and the lockdown containment measures improved the air quality significantly which can be inferred from the day and the night time concentrations of PM2.5 and PM10 during summer season.

Air Quality Status in Konya City Centre, Konya, Turkey during Pandemic Covid-19

High developed industry regions, provincial centers with a heavy traffic and dense populations with cold winters, using low-quality fossil fuel consumption have an effect on quality of life especially for people with respiratory diseases. The air quality data gathered from air monitoring stations for the City Center of Konya, Turkey were analysed statistically during the period when curfew due to the Covid-19 pandemic in 2020. The restrictions that cause the reduction of vehicle exhaust emissions, which are important factors in the formation of some air pollutants, are thought to be effective in improving the air quality as well as meteorological conditions are effective on the days when the air quality is analysed. It is thought that the reduction in HC, NOx, CO and PM emissions, which can be evaluated as exhaust emissions, will be an effective factor due to the restrictions of Covid-19 pandemic. Comparing between the 2019 and 2020 MCO data, we identified that most of the gases decreased with NO2 (-24 µg/m3), SO2 (-24 µg/m3), CO (-37 µg/m3) and an increment of O3 of +50 µg/m3 which indicates that the MCO and restricted of movement were give an impact to air quality levels in Konya City. The increase in O3 values were found by the existing of the sun rays in the atmosphere with the formation of O3 during the clean air period.

Ozone pollution during the COVID-19 lockdown in the spring of 2020 over Europe, analysed from satellite observations, in situ measurements, and models

We present a comprehensive study integrating satellite observations of ozone pollution, in situ measurements, and chemistry-transport model simulations for quantifying the role of anthropogenic emission reductions during the COVID-19 lockdown in spring 2020 over Europe. Satellite observations are derived from the IASI+GOME2 (Infrared Atmospheric Sounding Interferometer + Global Ozone Monitoring Experiment 2) multispectral synergism, which provides better sensitivity to near-surface ozone pollution. These observations are mainly analysed in terms of differences between the average on 1–15 April 2020, when the strictest lockdown restrictions took place, and the same period in 2019. They show clear enhancements of near-surface ozone in central Europe and northern Italy, as well as some other hotspots, which are typically characterized by volatile organic compound (VOC)-limited chemical regimes. An overall reduction of ozone is observed elsewhere, where ozone chemistry is limited by the abundance of NOx. The spatial distribution of positive and negative ozone concentration anomalies observed from space is in relatively good quantitative agreement with surface in situ measurements over the continent (a correlation coefficient of 0.55, a root-mean-squared difference of 11 ppb, and the same standard deviation and range of variability). An average difference of ∼ 8 ppb between the two observational datasets is observed, which can partly be explained by the fact the satellite approach retrieves partial columns of ozone with a peak sensitivity above the surface (near 2 km of altitude over land and averaging kernels reaching the middle troposphere over ocean).For assessing the impact of the reduction of anthropogenic emissions during the lockdown, we adjust the satellite and in situ surface observations for subtracting the influence of meteorological conditions in 2020 and 2019. This adjustment is derived from the chemistry-transport model simulations using the meteorological fields of each year and identical emission inventories. Using adjustments adapted for the altitude and sensitivity of each observation, both datasets show consistent estimates of the influence of lockdown emission reduction. They both show lockdown-associated ozone enhancements in hotspots over central Europe and northern Italy, with a reduced amplitude with respect to the total changes observed between the 2 years and an overall reduction elsewhere over Europe and the ocean. Satellite observations additionally provide the ozone anomalies in the regions remote from in situ sensors, an enhancement over the Mediterranean likely associated with maritime traffic emissions, and a marked large-scale reduction of ozone elsewhere over ocean (particularly over the North Sea), in consistency with previous assessments done with ozone sonde measurements in the free troposphere.These observational assessments are compared with model-only estimations, using the CHIMERE chemistry-transport model. Whereas a general qualitative consistency of positive and negative ozone anomalies is observed with respect to observational estimates, significant changes are seen in their amplitudes. Models underestimate the range of variability of the ozone changes by at least a factor 2 with respect to the two observational datasets, both for enhancements and decreases of ozone. Moreover, a significant ozone decrease observed at a large hemispheric scale is not simulated since the modelling domain is the European continent. As simulations only consider the troposphere, the influence from stratospheric ozone is also missing. Sensitivity analyses also show an important role of vertical mixing of atmospheric constituents, which depends on the meteorological fields used in the simulation and significantly modify the amplitude of the changes of ozone pollution during the lockdown.

Assessing the Impact of Traffic Emissions on Fine Particulate Matter and Carbon Monoxide Levels in Hanoi through COVID-19 Social Distancing Periods

Hanoi, Vietnam, is usually ranked as one of the most polluted capital cities in terms of air quality, particularly PM2.5. However, there has not been enough data to determine the main source of this pollution. In this study, we utilized the rare opportunity of the COVID-19 social distancing to assess the contribution of traffic emission to PM2.5 and CO levels when traffic volume was reduced significantly in Hanoi. Hourly PM2.5 and CO concentrations were measured from nine urban and traffic monitoring stations during pre-, soft, hard, and post-social distancing periods. As a result, we observed large reductions in both PM2.5 and CO levels during social distancing periods. PM2.5 concentrations were 14–18% lower during the social distancing than before this period, while CO concentrations had a more considerable drop by 28–41%. It is known that meteorological conditions can have significant effects on the ambient levels of air pollutants. To overcome this challenge, weather normalized concentrations of those pollutants were estimated using the random forest model, a machine learning technique. The normalized weather concentrations showed smaller reductions by 7–10% for PM2.5 and 5–11% for CO, indicating the presence of favorable weather conditions for better air quality during the social distancing period. In further analysis, the apparent improvement of air quality in Hanoi during the social distancing period was in line with reducing traffic emissions while emissions from coal-fired power plants remained relatively stable.

Assessment of Reductions in Emission-driven Air Pollution during the Beijing Olympic Games, Shanghai World Expo, Guangzhou Asian Games and Wuhan COVID-19 Lockdown

Quantifying and comparing the effectiveness of different emission control strategies can provide insights for policy design and air quality management. In our previous work, we developed a wind-pollution decomposition (WPD) method that provides a robust tool to quantify meteorology-driven and emission-driven impacts on changes in air quality. In this study, we applied this method to quantify emission-driven impacts on the observed air quality changes during the three largest international socioeconomic mega-events in China, namely, Shanghai World Expo in 2010, Beijing Olympic Games in 2008, and Guangzhou Asian Games in 2010. We also applied the method to the air quality variation during the lockdown period in Wuhan due to COVID-19 and compared the emission-driven impacts on air quality among these events. The results quantitatively show that the emission-driven factor generally played a much stronger role (> 86%);the meteorology-driven factor promoted pollution mitigation during Wuhan, Beijing and Guangzhou events but worsened the air quality during Shanghai event. The emission-driven pollution reduction was largest in the Wuhan COVID-19 lockdown (64% NO2, 54% PM2.5 reductions), followed by Beijing Olympics (42% PM2.5, 31% NO2 reductions), The Wuhan COVID-19 impact on air quality improvement is not as effective as expected especially for O3, which implies the difficulty of air quality attainment under normal, non-lockdown daysComparison of these events show that shutdown or emission control measures applied to industries and power plants were generally benefit for PM2.5, SO2 and NO2 reduction, while those applied to on-road traffic control are less-effective for reducing NO2 and not works for the mean O3 reduction. The results imply that advanced control measures for vehicle exhaust and control strategies considering the interaction between O3 and NOx/VOC/PM are necessary. In addition, the ongoing supervision of control strategies implementation is one of the key issues for future air quality management in China.

MODELLING VEHICLE EMISSIONS USING ARTIFICIAL NEURAL NETWORK AND MULTIPLE LINEAR REGRESSION METHODS

The road transport sector plays a vital role in economic development and vehicle numbers are growing. It provides a set of services to meet the different demands of travel and it is a necessity for human civilization. However, although it is an essential element in regional development schemes, it generates negative externalities, thus constituting one of the most important sources of environmental pollution. This paper aims to develop modelling vehicle emissions, especially, the HC, CO and NOx based on experimental speed profiles, acceleration and technical parameters related to the used vehicle. This helps to determine and study vehicle emissions factor related to different pollutant. Two methods are used to develop two different empirical models: the multiple regression and Artificial Neural Network (ANN). The developed approach was applied to two types of vehicle with different technical characteristics. It was observed that the multiple linear regression method allows to predict vehicle emissions with a coefficient of determination between 0.723 and 0.921 but the ANN model can predict exhaust gases with a correlation coefficient in the range of 0.95-0.99. Simulation results demonstrate the efficiency and superiority of the ANN tool to estimate vehicle emissions compared to multiple linear regression approach.

Impacts of emergency health protection measures upon air quality, traffic and public health: evidence from Oxford, UK.

Emergency responses to the COVID-19 pandemic led to major changes in travel behaviours and economic activities in 2020. Machine learning provides a reliable approach for assessing the contribution of these changes to air quality. This study investigates impacts of health protection measures upon air pollution and traffic emissions and estimates health and economic impacts arising from these changes during two national 'lockdown' periods in Oxford, UK. Air quality improvements were most marked during the first lockdown with reductions in observed NO2 concentrations of 38% (SD ± 24.0%) at roadside and 17% (SD ± 5.4%) at urban background locations. Observed changes in PM2.5, PM10 and O3 concentrations were not significant during first or second lockdown. Deweathering and detrending analyses revealed a 22% (SD ± 4.4%) reduction in roadside NO2 and 2% (SD ± 7.1%) at urban background with no significant changes in the second lockdown. Deweathered-detrended PM2.5 and O3 concentration changes were not significant, but PM10 increased in the second lockdown only. City centre traffic volume reduced by 69% and 38% in the first and second lockdown periods. Buses and passenger cars were the major contributors to NO2 emissions, with relative reductions of 56% and 77% respectively during the first lockdown, and less pronounced changes in the second lockdown. While car and bus NO2 emissions decreased during both lockdown periods, the overall contribution from buses increased relative to cars in the second lockdown. Sustained NO2 emissions reduction consistent with the first lockdown could prevent 48 lost life-years among the city population, with economic benefits of up to £2.5 million. Our findings highlight the critical importance of decoupling emissions changes from meteorological influences to avoid overestimation of lockdown impacts and indicate targeted emissions control measures will be the most effective strategy for achieving air quality and public health benefits in this setting.

Characterization of the vehicle emissions in the Greater Taipei Area through vision-based traffic analysis system and its impacts on urban air quality.

In recent years, many surveillance cameras have been installed in the Greater Taipei Area, Taiwan; traffic data obtained from these surveillance cameras could be useful for the development of roadway-based emissions inventories. In this study, web-based traffic information covering the Greater Taipei Area was obtained using a vision-based traffic analysis system. Web-based traffic data were normalized and applied to the Community Multiscale Air Quality (CMAQ) model to study the impact of vehicle emissions on air quality in the Greater Taipei Area. According to an analysis of the obtained traffic data, sedans were the most common vehicles in the Greater Taipei Area, followed by motorcycles. Moderate traffic conditions with an average speed of 30-50 km/h were most prominent during weekdays, whereas traffic flow with an average speed of 50-70 km/h was most common during weekends. The proportion of traffic flows in free-flow conditions (>70 km/h) was higher on weekends than on weekdays. Two peaks of traffic flow were observed during the morning and afternoon peak hours on weekdays. On the weekends, this morning peak was not observed, and the variation in vehicle numbers was lower than on weekdays. The simulation results suggested that the addition of real-time traffic data improved the CMAQ model's performance, especially for the carbon monoxide (CO) and fine particulate matter (PM2.5) concentrations. According to sensitivity tests for total and vehicle emissions in the Greater Taipei Area, vehicle emissions contributed to >90% of CO, 80% of nitrogen oxides (NOx), and approximately 50% of PM2.5 in the downtown areas of Taipei. The vehicle emissions contribution was affected by both vehicle emissions and meteorological conditions. The connection between the surveillance camera data, vehicle emissions, and regional air quality models in this study can also be used to explore the impact of special events (e.g., long weekends and COVID-19 lockdowns) on air quality.

Effect of COVID-19 travel restrictions on Phoenix air quality after accounting for boundary layer variations.

Due to the global response to the COVID-19 pandemic, there have been a variety of policy responses that have produced a range of expected and unexpected effects on society and our surrounding environment. One widely reported result of the pandemic response is that travel restrictions have resulted in improvements in regional air quality. This study aims to determine the effect of COVID-19 related Stay at Home precautions on air quality in a metropolitan area. We specifically focus on CO, NO2, and PM10 in Maricopa County (Phoenix), Arizona, as these all contribute to local air quality concerns. The role of meteorological parameters on ambient concentrations for these pollutants was investigated by using the local planetary boundary layer height (PBH) to account for vertical mixing. Across all three sites studied, there was no uniform decrease in either CO or NO2, even when freeway traffic volume was down by ~35%. For PM10, there was a significant decrease of ~45% seen at all the sites for the period most directly impacted by local Stay at Home restrictions compared to the past two years. This indicates that different pollutants have fundamentally different behavior in the local environment and suggests that these pollutants originate from different sources.