Probabilistic Health Risk Assessment of Vehicular Emissions as an Urban Health Indicator in Dhaka City

Changes in Air Quality and Drivers for the Heavy PM2.5 Pollution on the North China Plain Pre- to Post-COVID-19

Under the clean air action plans and the lockdown to constrain the coronavirus disease 2019 (COVID-19), the air quality improved significantly. However, fine particulate matter (PM2.5) pollution still occurred on the North China Plain (NCP). This study analyzed the variations of PM2.5, nitrogen dioxide (NO2), sulfur dioxide (SO2), carbon monoxide (CO), and ozone (O3) during 2017-2021 on the northern (Beijing) and southern (Henan) edges of the NCP. Furthermore, the drivers for the PM2.5 pollution episodes pre- to post-COVID-19 in Beijing and Henan were explored by combining air pollutant and meteorological datasets and the weighted potential source contribution function. Results showed air quality generally improved during 2017-2021, except for a slight rebound (3.6%) in NO2 concentration in 2021 in Beijing. Notably, the O3 concentration began to decrease significantly in 2020. The COVID-19 lockdown resulted in a sharp drop in the concentrations of PM2.5, NO2, SO2, and CO in February of 2020, but PM2.5 and CO in Beijing exhibited a delayed decrease in March. For Beijing, the PM2.5 pollution was driven by the initial regional transport and later secondary formation under adverse meteorology. For Henan, the PM2.5 pollution was driven by the primary emissions under the persistent high humidity and stable atmospheric conditions, superimposing small-scale regional transport. Low wind speed, shallow boundary layer, and high humidity are major drivers of heavy PM2.5 pollution. These results provide an important reference for setting mitigation measures not only for the NCP but for the entire world.

Determination of Heavy Metal Contamination and Pollution Indices of Roadside Dust in Dhaka City, Bangladesh

Urban roadside dust samples from Dhaka City in Bangladesh were collected from a planned residential area (PRA), spontaneous residential area (SRA), commercial area (CA), and urban green area (UGA) in winter and summer to study how season and different urban land-use categories influence the concentrations of heavy metals (Cr, Mn, Co, Ni, Cu, Zn, As, and Pb) and different pollution indices. The dust samples were fractionated into <32 μm particles, extracted by acid digestion followed by estimation of heavy metals, using ICP-MS. Pollution indices were calculated from the metal concentrations, using standard protocols. The concentrations of heavy metals in roadside dust varied significantly (all p < 0.05), due to sampling seasons and the land-use category. Higher concentrations of heavy metals (Cr, Mn, Ni, Cu, Zn, and Pb) were found in the dust sampled during the winter season than in the summer season, except for As and Co. The geo-accumulation index (Igeo) indicated that the commercial area was heavily contaminated with Cu and Zn during the winter season. The contamination factor (CF) was higher for Cu and Zn in the CA, PRA, and SRA of Dhaka City in winter than in the summer season. The enrichment factor (EF) suggested that Mn and Co were the least enriched metals, and significant enrichment was seen for Cu and Zn for all land-use categories, both in summer and winter. A moderate potential ecological risk for Cu was estimated in CA and PRA in the winter season.

Probabilistic total PM2.5 emissions from vehicular sources in Australian perspective

Motor vehicles operating on the road are a significant source of Particulate Matter (PM) emissions depending on the fuels used in the vehicles. Gasoline and Diesel vehicles are directly responsible for the tailpipe PM emissions (specifically PM_2.5: particles ≤ 2.5 µm), known as primary PM_2.5 emissions. The other major direct emissions from the vehicles, which include volatile organic compounds (VOCs), and nitrogen oxides (NOx) contribute to the formation of secondary organic PM, also known as secondary organic aerosols (SOA), through some inter-related chemical reactions. The SOAs are highly toxic and contribute to a portion of total PM emissions. In this research, emission scenarios of both primary PM_2.5 and SOA for a car-dependent expanding Australian city (Adelaide) were analyzed. The variability of traffic characteristics on road was considered and conducted a probabilistic emissions inventory for tailpipe primary PM_2.5 and precursors, while statistical analysis of the probable chemical conversion ratios was considered for the SOA inventory. It was found that the tailpipe emissions from the vehicles were higher than the air quality standard, while the SOA contribution from the vehicles was not significantly high but contributed to the increase of total PM concentration. The analysis of the chemical transformation of SOA precursors justified the importance of conducting more detailed emissions modelling for sustainable urban air quality planning.

Vehicular PM Emissions and Urban Public Health Sustainability: A Probabilistic Analysis for Dhaka City

Particulate Matter (PM) pollution is generally considered as a prime indicator of urban air quality and is linked to human health hazards. As vehicles are a vital component of an urban setting, the risks of particulate pollution need to be assessed. An emission modelling is essential for that, and thus stochastic modelling approach involving Monte Carlo simulation technique was applied, aiming to reduce the uncertainty in emission modelling. The risks scenarios for the emissions were generated for 2019 (present state) and 2024 (future), integrating the probability of emissions and the associated AQI (Air Quality Index). Despite the vehicles being a minor source of PM in Dhaka (compared to the contribution from other sources), about one-third of the city is found under high risk due to the exhaust particulate pollution; having the potentiality to cover more than 60% of the city in the coming years, affecting the urban public health sustainability. However, the extent of implementation of planning and management strategies can revert the scenarios for the city, which can plausibly reduce the risk from 80% to 50%, or even to a no-risk state.