Counterfactual time series analysis of short-term change in air pollution following the COVID-19 state of emergency in the United States

Lockdown measures implemented in response to the COVID-19 pandemic produced sudden behavioral changes. We implement counterfactual time series analysis based on seasonal autoregressive integrated moving average models (SARIMA), to examine the extent of air pollution reduction attained following state-level emergency declarations. We also investigate whether these reductions occurred everywhere in the US, and the local factors (geography, population density, and sources of emission) that drove them. Following state-level emergency declarations, we found evidence of a statistically significant decrease in nitrogen dioxide (NO2) levels in 34 of the 36 states and in fine particulate matter (PM2.5) levels in 16 of the 48 states that were investigated. The lockdown produced a decrease of up to 3.4 µg/m3 in PM2.5 (observed in California) with range (− 2.3, 3.4) and up to 11.6 ppb in NO2 (observed in Nevada) with range (− 0.6, 11.6). The state of emergency was declared at different dates for different states, therefore the period "before" the state of emergency in our analysis ranged from 8 to 10 weeks and the corresponding "after" period ranged from 8 to 6 weeks. These changes in PM2.5 and NO2 represent a substantial fraction of the annual mean National Ambient Air Quality Standards (NAAQS) of 12 µg/m3 and 53 ppb, respectively. As expected, we also found evidence that states with a higher percentage of mobile source emissions (obtained from 2014) experienced a greater decline in NO2 levels after the lockdown. Although the socioeconomic restrictions are not sustainable, our results provide a benchmark to estimate the extent of achievable air pollution reductions. Identification of factors contributing to pollutant reduction can help guide state-level policies to sustainably reduce air pollution.

Development of an Analytical Line Source Dispersion Model to Predict Ground Level Concentrations for Particulate Matter (PM) of Different Particle Size Ranges

Particulate matter (PM) is released in varying quantities from mobile sources depending on the type of fossil fuel used in combustion. According to the USEPA, PM exposure could cause a variety of problems such as premature deaths, nonfatal heart attacks, irregular heartbeat, asthma, reduced lung function, and respiratory issues. Therefore, it is necessary to predict the downwind concentrations near highways from mobile sources to protect the public from adverse health effects. The current study concentrates on developing an analytical line source dispersion model to account for different particle size ranges for particulate matter released from mobile sources. Available line source models do not explicitly consider different ranges of particle sizes present in the exhaust. The present study discusses the development of a dispersion model to predict downwind concentrations of PM by incorporating a range of particle sizes for an infinite and a finite-length mobile source. The dry deposition of particles is also considered during development. The emission rate, wind speed, wind direction, atmospheric turbulence, and dry deposition velocity of the particles are the model inputs. The sensitivity of the model is determined by simultaneously varying the independent input variables using Monte Carlo simulation by Crystal Ball software. The sensitivity analysis results generated using Crystal Ball are preliminary in nature and should be re-examined.

The Effect of Lockdown Period During Covid-19 Pandemic on Air Quality in Sydney Region, Australia

In early 2020 from April to early June, the metropolitan area of Sydney as well as the rest of New South Wales (NSW, Australia) experienced a period of lockdown to prevent the spread of Covid-19 virus in the community. The effect of reducing anthropogenic activities including transportation had an impact on the urban environment in term of air quality which is shown to have improved for a number of pollutants, such as nitrogen dioxides (NO2) and carbon monoxide (CO), based on monitoring data on ground and from satellite. Besides primary pollutants CO and NOx emitted from mobile sources, PM2.5 (primary and secondary) and secondary ozone (O3) during the lockdown period will also be analysed using both air quality data and modelling method. The results show that NO2, CO and PM2.5 levels decreased during the lockdown, but O3 instead increased. The change in the concentration levels however are small considering the large reduction in traffic volume of ~30%. By estimate the decrease in traffic volume in Sydney region, the corresponding decrease in emission input to the WRF-CMAQ (Weather Research and Forecasting - Community Multiscale Air Quality Modeling System) air quality model is then used to estimate the effect of lockdown on the air quality especially CO, NO2, O3 and PM2.5 in the Greater Metropolitan Region (GMR) of Sydney. COVID-19 lockdown period is an ideal case to study the effect of motor vehicle and mobile source contribution to air pollutants such as those listed above in the GMR.

On-Road Portable Emission Measurement Systems Test Data Analysis and Light-Duty Vehicle In-Use Emissions Development

Portable emission measurement systems (PEMS) [1] are used by the US Environmental Protection Agency (EPA) to measure gaseous and particulate matter mass emissions from vehicles in normal, in-use, on-the-road, and “real-world” operations to support many of its programs. These programs include vehicle modeling, emissions compliance, regulatory development, emissions inventory development, and investigations of the effects of real, in-use driving conditions on NOx, CO2, and other regulated pollutants. This article discusses EPA’s analytical methodology for evaluating light-duty vehicle energy and EU Real Driving Emissions (RDE). A simple, data-driven model was developed and validated using measured PEMS emissions test data. The work also included application of the EU RDE procedures and comparison to the PEMS test methodologies and FTP and other chassis dynamometer test data used by EPA for characterizing in-use light- and heavy-duty vehicle emissions. This work was conducted as part of EPA’s participation in the development of UNECE Global Technical Regulations and also supports EPA mobile source emission inventory development. This article discusses the real-world emissions of light-duty vehicles with 12V Start-Stop technology and light-duty vehicles using both gasoline and diesel fuels.