key: cord-0828233-kff4z6wd
authors: Cameletti, Michela
title: The Effect of Corona Virus Lockdown on Air Pollution: Evidence from the City of Brescia in Lombardia Region (Italy)
date: 2020-10-15
journal: Atmos Environ (1994)
DOI: 10.1016/j.atmosenv.2020.117794
sha: 62370ad1cd8075e998ee225b1fbb33ee54091715
doc_id: 828233
cord_uid: kff4z6wd

After the outbreak of Corona virus pandemic in Italy, the government has taken extraordinary measures, including a national lockdown, to prevent the spread of the infection. This extraordinary situation has led to a reduction in air pollution levels measured in the whole Po Valley, usually known as one of the most polluted areas in Europe in terms of particulate matter (PM) and nitrogen dioxide (NO [Formula: see text]) concentrations. The main aim of this paper is to evaluate the effectiveness of the lockdown on the air quality improvement. In particular, an interrupted time series modelling approach is employed to test if a significant change in the level and the trend of the pollutant time series has occurred after the lockdown measure. The case study regards the city of Brescia (Northern Italy) and focuses on the comparison of the period before (January 1st–March 7th, 2020) and after (March 8th–March 27th, 2020) the lockdown. By adjusting for meteorology and Sunday effect, the results show that a significant change in air quality occurring in the post intervention period was observed only for a single NO [Formula: see text] station located in a heavy traffic zone. In particular, the estimate of the time series slope, i.e. the expected change in the concentration associated with a time unit increase, decreases from -0.25 to -1.67 after the lockdown. For the remaining stations, no significant change was found in the concentration time series when comparing the two periods. This confirms the complexity of air pollutant concentration dynamics for the considered area, which is not merely related to emission sources but depends also on other factors as, for example, (micro and macro) meteorological conditions and the chemical and physical processes in the atmosphere, which are all independent of the lockdown measure.

Po Valley in Northern Italy is known as one of the most 18 polluted areas in Europe in terms of particulate matter (PM) 19 and nitrogen dioxide (NO 2 ) concentration [6] . This is due 20 to the peculiar geographical structure of the area and the ex- Table 1 Information about monitoring stations and sensors in Brescia for NO 2 and PM 10 concentrations and meteorological variables (temperature, precipitation and wind speed).

Variable Table 1 where the details about the stations and sensors are reported. 

In particular, the regression error term follows an ARMA Table 3 ). This could be In Table 4 Table 1 ) and year, separately for the two considered periods: January 1st -March 7th and March 8th -March 27th. The white point represents the mean. Table 1 ) and year, separately for the two considered periods: January 1st -March 7th and March 8th -March 27th. The white point represents the mean. Gaussian white-noise.

The effect of Corona virus lockdown on air pollution Table 2 Estimates of and parameters (and corresponding pvalues) for the ITS models implemented for NO 2 time series. If the information is missing it means that the corresponding coefficient was not significantly different from zero and was removed from the model. Besides the covariate coefficients , the following parameters are estimated: the intercept 0 , the baseline trend slope 1 and the post-intervention level (trend slope) change 2 ( 3 ); see Eq.(1). In particular, 0 + 2 + and 1 + 3 represent the level and the trend slope after the lockdown, to be compared with 0 + and 1 , respectively. Table 3 Estimates of and parameters (and corresponding pvalues) for the ITS models implemented for PM 10 time series. If the information is missing it means that the corresponding coefficient was not significantly different from zero and was removed from the model. Besides the covariate coefficients , the following parameters are estimated: the intercept 0 , the baseline trend slope 1 and the post-intervention level (trend slope) change 2 ( 3 ); see Eq.(1). In particular, 0 + 2 + and 1 + 3 represent the level and the trend slope after the lockdown, to be compared with 0 + and 1 , respectively. 

In this paper ITS ARMA models are employed to assess publications/air-quality-in-europe-2019.

[7] Grundy, C., Steinbach, R., Edwards, P., Green, J., Armstrong, B., 

Interrupted time 337 series regression for the evaluation of public health interventions: a

Sta-393 tistical modeling of the early-stage impact of a new traffic policy in 394 milan, italy

Impact of covid-19 lockdown 398 on pm10, so2 and no2 concentrations in salè city (morocco)

R: A Language and Environment for Statistical 403

Changes 407 in air quality during the lockdown in barcelona (spain) one 408 month into the sars-cov-2 epidemic

Segmented regression analysis of interrupted time series studies in 414 medication use research