Dataset Information

A multi-step machine learning approach to assess the impact of COVID-19 lockdown on NO₂ attributable deaths in Milan and Rome, Italy.

ABSTRACT:

Background

Air pollution is one of the main concerns for the health of European citizens, and cities are currently striving to accomplish EU air pollution regulation. The 2020 COVID-19 lockdown measures can be seen as an unintended but effective experiment to assess the impact of traffic restriction policies on air pollution. Our objective was to estimate the impact of the lockdown measures on NO₂ concentrations and health in the two largest Italian cities.

Methods

NO₂ concentration datasets were built using data deriving from a 1-month citizen science monitoring campaign that took place in Milan and Rome just before the Italian lockdown period. Annual mean NO₂ concentrations were estimated for a lockdown scenario (Scenario 1) and a scenario without lockdown (Scenario 2), by applying city-specific annual adjustment factors to the 1-month data. The latter were estimated deriving data from Air Quality Network stations and by applying a machine learning approach. NO₂ spatial distribution was estimated at a neighbourhood scale by applying Land Use Random Forest models for the two scenarios. Finally, the impact of lockdown on health was estimated by subtracting attributable deaths for Scenario 1 and those for Scenario 2, both estimated by applying literature-based dose-response function on the counterfactual concentrations of 10 μg/m³.

Results

The Land Use Random Forest models were able to capture 41-42% of the total NO₂ variability. Passing from Scenario 2 (annual NO₂ without lockdown) to Scenario 1 (annual NO₂ with lockdown), the population-weighted exposure to NO₂ for Milan and Rome decreased by 15.1% and 15.3% on an annual basis. Considering the 10 μg/m³ counterfactual, prevented deaths were respectively 213 and 604.

Conclusions

Our results show that the lockdown had a beneficial impact on air quality and human health. However, compliance with the current EU legal limit is not enough to avoid a high number of NO₂ attributable deaths. This contribution reaffirms the potentiality of the citizen science approach and calls for more ambitious traffic calming policies and a re-evaluation of the legal annual limit value for NO₂ for the protection of human health.

SUBMITTER: Boniardi L

PROVIDER: S-EPMC8761378 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:Background The standard for diagnosis of SARS-CoV-2 virus is reverse transcription polymerase chain reaction (RT-PCR) test, but chest CT may play a complimentary role in the early detection of COVID-19 pneumonia. Purpose To investigate CT features of patients with COVID-19 in Rome, Italy, and to compare the accuracy of CT with RT-PCR. Methods In this prospective study from March 4, 2020, until March 19, 2020, consecutive patients with suspected COVID-19 infection and respiratory symptoms were enrolled. Exclusion criteria were: chest CT with contrast medium performed for vascular indications, patients who refused chest CT or hospitalization, and severe CT motion artifact. All patients underwent RT-PCR and chest CT. Diagnostic performance of CT was calculated using RT-PCR as reference. Chest CT features were calculated in a subgroup of RT-PCR-positive and CT-positive patients. CT features of hospitalized patients and patient in home isolation were compared by using Pearson chi squared test. Results Our study population comprised 158 consecutive study participants (83 male and 75 female, mean age 57 y ±17). Fever was observed in 97/158 (61%), cough in 88/158 (56%), dyspnea in 52/158 (33%), lymphocytopenia in 95/158 (60%), increased C-reactive protein level in 139/158 (88%), and elevated lactate dehydrogenase in 128/158 (81%) study participants. Sensitivity, specificity, and accuracy of CT were 97% (60/62)[95% IC, 88-99%], 56% (54/96)[95% IC,45-66%] and 72% (114/158)[95% IC 64-78%], respectively. In the subgroup of RT-PCR-positive and CT-positive patients, ground-glass opacities (GGO) were present in 58/58 (100%), multilobe and posterior involvement were both present in 54/58 (93%), bilateral pneumonia in 53/58 (91%), and subsegmental vessel enlargement (> 3 mm) in 52/58 (89%) of study participants. Conclusion The typical pattern of COVID-19 pneumonia in Rome, Italy, was peripherally ground-glass opacities with multilobe and posterior involvement, bilateral distribution, and subsegmental vessel enlargement (> 3 mm). Chest CT sensitivity was high (97%) but with lower specificity (56%).

Dataset Information

A multi-step machine learning approach to assess the impact of COVID-19 lockdown on NO₂ attributable deaths in Milan and Rome, Italy.

Background

Methods

Results

Conclusions

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets

Dataset Information

A multi-step machine learning approach to assess the impact of COVID-19 lockdown on NO2 attributable deaths in Milan and Rome, Italy.

Background

Methods

Results

Conclusions

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets

A multi-step machine learning approach to assess the impact of COVID-19 lockdown on NO₂ attributable deaths in Milan and Rome, Italy.