Chemical composition and potential sources of PM2.5 in Hanoi

The chemical composition of PM2.5 was monitored simultaneously at two sites, one in a general area of the city center and one at a roadside, in Hanoi, Vietnam, during August 2019–July 2020 using 220 daily (24 h) filter samples. PM mass, water soluble ions, trace elements, organic and elemental carbon and sugar anhydrides were measured. The annual average PM2.5 concentrations, 49 and 46 μg m−3 at the traffic and the general urban site, respectively, exceeded the national (25 μg m−3) and 2021 WHO limit values (5 μg m−3). Daily PM2.5 concentrations were the highest in winter when stagnant meteorological conditions prevailed. On average, half of the resolved mass was organic matter, of which about 40% was attributable to biomass burning, most likely rice straw field burning and domestic fuel combustion. One third of PM2.5 was secondary inorganic aerosol which was dominated by sulphate hence indicating a high contribution of stationary sources like coal combustion. The elemental carbon level was higher at the traffic site, except in April 2020 during the COVID-19 restrictions. Zinc was the most common trace element with high daily variations and large differences between the sites, and it often peaked with Cd, Cl and Pb indicating contribution of industrial sources and/or coal combustion. The highest zinc concentrations appeared on a few days and likely originated from open burning of municipal solid waste. It appeared that scattered open waste and biomass burning, as well as coal combustion, are important sources causing spikes of PM2.5 pollution in Hanoi above the general levels caused by routine industrial and traffic sources, especially during stagnant winter days. Source contributions were further studied with positive matrix factorization producing six source factors: traffic (12%), local secondary inorganic aerosol (SIA, 18%), biomass burning (19%), industry (9%), long-range transported SIA (25%) and dust (17%).

Differential impact of government lockdown policies on reducing air pollution levels and related mortality in Europe.

Previous studies have reported a decrease in air pollution levels following the enforcement of lockdown measures during the first wave of the COVID-19 pandemic. However, these investigations were mostly based on simple pre-post comparisons using past years as a reference and did not assess the role of different policy interventions. This study contributes to knowledge by quantifying the association between specific lockdown measures and the decrease in NO2, O3, PM2.5, and PM10 levels across 47 European cities. It also estimated the number of avoided deaths during the period. This paper used new modelled data from the Copernicus Atmosphere Monitoring Service (CAMS) to define business-as-usual and lockdown scenarios of daily air pollution trends. This study applies a spatio-temporal Bayesian non-linear mixed effect model to quantify the changes in pollutant concentrations associated with the stringency indices of individual policy measures. The results indicated non-linear associations with a stronger decrease in NO2 compared to PM2.5 and PM10 concentrations at very strict policy levels. Differences across interventions were also identified, specifically the strong effects of actions linked to school/workplace closure, limitations on gatherings, and stay-at-home requirements. Finally, the observed decrease in pollution potentially resulted in hundreds of avoided deaths across Europe.

Estimating lockdown-induced European NO2 changes using satellite and surface observations and air quality models

This study provides a comprehensive assessment of NO2 changes across the main European urban areas induced by COVID-19 lockdowns using satellite retrievals from the Tropospheric Monitoring Instrument (TROPOMI) onboard the Sentinel-5p satellite, surface site measurements, and simulations from the Copernicus Atmosphere Monitoring Service (CAMS) regional ensemble of air quality models. Some recent TROPOMI-based estimates of changes in atmospheric NO2 concentrations have neglected the influence of weather variability between the reference and lockdown periods. Here we provide weather-normalized estimates based on a machine learning method (gradient boosting) along with an assessment of the biases that can be expected from methods that omit the influence of weather. We also compare the weather-normalized satellite-estimated NO2 column changes with weather-normalized surface NO2 concentration changes and the CAMS regional ensemble, composed of 11 models, using recently published estimates of emission reductions induced by the lockdown. All estimates show similar NO2 reductions. Locations where the lockdown measures were stricter show stronger reductions, and, conversely, locations where softer measures were implemented show milder reductions in NO2 pollution levels. Average reduction estimates based on either satellite observations (-23 %), surface stations (-43 %), or models (-32 %) are presented, showing the importance of vertical sampling but also the horizontal representativeness. Surface station estimates are significantly changed when sampled to the TROPOMI overpasses (-37 %), pointing out the importance of the variability in time of such estimates. Observation-based machine learning estimates show a stronger temporal variability than model-based estimates.