In-kitchen air pollution is a leading environmental issue, attributable to extensive cooking, poor ventilation and the use of polluting fuels. We carried out a week-long monitoring of CO2, temperature and relative humidity (RH) in five low-income residential kitchens of 12 global cities (Dhaka, Chennai, Nanjing, Medellín, São Paulo, Cairo, Sulaymaniyah, Addis Ababa, Nairobi, Blantyre, Akure and Dar-es-Salaam). During cooking, the average in-kitchen CO2 concentrations were 22.2% higher than the daily indoor average. Also, the highest CO2 was observed for NVd (natural ventilation-door only; 711 ± 302 ppm), followed by NVdw (natural ventilation-door + window; 690 ± 319 ppm) and DVmn (dual ventilation-mechanical + natural; 677 ± 219 ppm). Using LPG and electric appliances during cooking exhibited 32.2% less CO2 than kerosene. Larger kitchens (46–120 m3) evinced 28% and 20% less CO2 than medium (16–45 m3) and small (4–15 m3) ones, respectively. In-kitchen CO2 with >2 occupants during cooking was 7% higher than that with one occupant. 87% of total kitchens exceeded the ASHRAE standard (RH >40%, temperature >23 °C) for thermal comfort. Considering the ventilation type, both the ACH (air change rate per hour) and ventilation rate followed the order: NVdw > NVd > DVmn, while the trend for weekly average CO2 concentration was NVd > DVmn > NVdw. Larger kitchens presented 22% and 28% less ACH, and 82% and 190% higher ventilation rate than medium- and small-volume ones, respectively. Forty-three percent kitchens had ACH
Cities are constantly evolving and so are the living conditions within and between them. Rapid urbanization and the ever-growing need for housing have turned large areas of many cities into concrete landscapes that lack greenery. Green infrastructure can support human health, provide socio-economic and environmental benefits, and bring color to an otherwise grey urban landscape. Sometimes, benefits come with downsides in relation to its impact on air quality and human health, requiring suitable data and guidelines to implement effective greening strategies. Air pollution and human health, as well as green infrastructure and human health, are often studied together. Linking green infrastructure with air quality and human health together is a unique aspect of this article. A holistic understanding of these links is key to enabling policymakers and urban planners to make informed decisions. By critically evaluating the link between green infrastructure and human health via air pollution mitigation, we also discuss if our existing understanding of such interventions is enabling their uptake in practice. Both the natural science and epidemiology approach the topic of green infrastructure and human health very differently. The pathways linking health benefits to pollution reduction by urban vegetation remain unclear and that the mode of green infrastructure deployment is critical to avoid unintended consequences. Strategic deployment of green infrastructure may reduce downwind pollution exposure. However, the development of bespoke design guidelines is vital to promote and optimize greening benefits and measuring green infrastructure’s socio-economic and health benefits are key for their uptake. Greening cities to mitigate pollution effects is on the rise and these needs to be matched by scientific evidence and appropriate guidelines. We conclude that urban vegetation can facilitate broad health benefits, but there is little empirical evidence linking these benefits to air pollution reduction by urban vegetation, and appreciable efforts are needed to establish the underlying policies, design and engineering guidelines governing its deployment.
The COVID-19 pandemic elicited a global response to limit associated mortality, with social distancing and lockdowns being imposed. In India, human activities were restricted from late March 2020. This ‘anthropogenic emissions switch-off’ presented an opportunity to investigate impacts of COVID-19 mitigation measures on ambient air quality in five Indian cities (Chennai, Delhi, Hyderabad, Kolkata, and Mumbai), using in-situ measurements from 2015 to 2020. For each year, we isolated, analysed and compared fine particulate matter (PM2.5) concentration data from 25 March to 11 May, to elucidate the effects of the lockdown. Like other global cities, we observed substantial reductions in PM2.5 concentrations, from 19 to 43% (Chennai), 41–53 % (Delhi), 26–54 % (Hyderabad), 24–36 % (Kolkata), and 10–39 % (Mumbai). Generally, cities with larger traffic volumes showed greater reductions. Aerosol loading decreased by 29 % (Chennai), 11 % (Delhi), 4% (Kolkata), and 1% (Mumbai) against 2019 data. Health and related economic impact assessments indicated 630 prevented premature deaths during lockdown across all five cities, valued at 0.69 billion USD. Improvements in air quality may be considered a temporary lockdown benefit as revitalising the economy could reverse this trend. Regulatory bodies must closely monitor air quality levels, which currently offer a baseline for future mitigation plans.
A key challenge in controlling Delhi’s air quality is a lack of clear understanding of the impacts of emissions from the surrounding National Capital Region (NCR). Our objectives are to understand the limitations of publicly available data, its utility to determine pollution sources across Delhi-NCR and establish seasonal profiles of chemically active trace gases. We obtained the spatiotemporal characteristics of daily-averaged particulate matter (PM10 and PM2.5) and trace gases (NOX,O3,SO2, and CO) within a network of 12 air quality monitoring stations located over 2000 km2 across Delhi-NCR from January 2014 to December 2017. The highest concentrations of pollutants, except O3, were found at Anand Vihar compared with lowest at Panchkula. A high homogeneity in PM2.5 was observed among Delhi sites as opposed to a high spatial divergence between Delhi and NCR sites. The bivariate polar plots and k-means clustering showed that PM2.5 and PM10 concentrations are dominated by local sources for all monitoring sites across Delhi-NCR. A consequence of the dominance of local source contributions to measured concentrations, except to one site remote from Delhi, is that it is not possible to evaluate the influence of regional pollution transport upon PM concentrations measured at sites within Delhi and the NCR from concentration measurements alone.
Increasing emissions from sources such as construction and burning of biomass from crop residues, roadside and municipal solid waste have led to a rapid increase in the atmospheric concentrations of fine particulate matter (≤2.5 μm; PM2.5) over many Indian cities. Analyses of their chemical profiles are important for receptor models to accurately estimate the contributions from different sources. We have developed chemical source profiles for five important pollutant sources - construction (CON), paved road dust (PRD), roadside biomass burning (RBB), solid waste burning (SWB), and crop residue burning (CPB) - during three intensive campaigns (winter, summer and post-monsoon) in and around Delhi. We obtained chemical characterisations of source profiles incorporating carbonaceous material such as organic carbon (OC) and elemental carbon (EC), water-soluble ions (F−, Cl−, NO2−, NO3−, SO42−, PO43−, Na+ and NH4+), and elements (Mg, Al, Si, P, S, Cl, K, Ca, Ti, V, Cr, Mn, Fe, Co, Ni, Cu, Zn, As, Br, Rb, Sr, Ba, and Pb). CON was dominated by the most abundant elements, K, Si, Fe, Al, and Ca. PRD was also dominated by crustal elements, accounting for 91% of the total analysed elements. RBB, SWB and CPB profiles were dominated by organic matter, which accounted for 94%, 86.2% and 86% of the total PM2.5, respectively. The database of PM emission profiles developed from the sources investigated can be used to assist source apportionment studies for accurate quantification of the causes of air pollution and hence assist governmental bodies in formulating relevant countermeasures. [Display omitted]
Car microenvironments significantly contribute to the daily pollution exposure of commuters, yet health and socioeconomic studies focused on in-car exposure are rare. This study aims to assess the relationship between air pollution levels and socioeconomic indicators (fuel prices, city-specific GDP, road density, the value of statistical life (VSL), health burden and economic losses resulting from exposure to fine particulate matter ≤2.5µm; PM2.5) during car journeys in ten cities: Dhaka (Bangladesh); Chennai (India); Guangzhou (China); Medellín (Colombia); São Paulo (Brazil); Cairo (Egypt); Sulaymaniyah (Iraq); Addis Ababa (Ethiopia); Blantyre (Malawi); and Dar-es-Salaam (Tanzania). Data collected by portable laser particle counters were used to develop a proxy of car-user exposure profiles. Hotspots on all city routes displayed higher PM2.5 concentrations and disproportionately high inhaled doses. For instance, the time spent at the hotspots in Guangzhou and Addis Ababa was 26% and 28% of total trip time, but corresponded to 54% and 56%, respectively, of the total PM2.5 inhaled dose. With the exception of Guangzhou, all the cities showed a decrease in per cent length of hotspots with an increase in GDP and VSL. Exposure levels were independent of fuel prices in most cities. The largest health burden related to in-car PM2.5 exposure was estimated for Dar-es-Salam (81.6±39.3 μg m-3), Blantyre (82.9±44.0) and Dhaka (62.3±32.0) with deaths per 100,000 of the car commuting population per year of 2.46 (2.28-2.63), 1.11 (0.97-1.26) and 1.10 (1.05-1.15), respectively. However, the modest health burden of 0.07 (0.06-0.08), 0.10 (0.09-0.12) and 0.02 (0.02-0.03) deaths per 100,000 of the car commuting population per year were estimated for Medellin (23±13.7 μg m-3), São Paulo (25.6±11.7) and Sulaymaniyah (22.4±15.0), respectively. Lower GDP was found to be associated with higher economic losses due to health burdens caused by air pollution in most cities, indicating a socioeconomic discrepancy. This assessment of health and socioeconomic parameters associated with in-car PM2.5 exposure highlights the importance of implementing plausible solutions to make a positive impact on peoples’ lives in these cities.
Cars are a commuting lifeline worldwide, despite contributing significantly to air pollution. This is the first global assessment on air pollution exposure in cars across ten cities: Dhaka (Bangladesh); Chennai (India); Guangzhou (China); Medellín (Colombia); São Paulo (Brazil); Cairo (Egypt); Sulaymaniyah (Iraq); Addis Ababa (Ethiopia); Blantyre (Malawi); and Dar-es-Salaam (Tanzania). Portable laser particle counters were used to develop a proxy of car-user exposure profiles and analyse the factors affecting particulate matter ≤2.5 μm (PM2.5; fine fraction) and ≤10 μm (PM2.5–10; coarse fraction). Measurements were carried out during morning, off- and evening-peak hours under windows-open and windows-closed (fan-on and recirculation) conditions on predefined routes. For all cities, PM2.5 and PM10 concentrations were highest during windows-open, followed by fan-on and recirculation. Compared with recirculation, PM2.5 and PM10 were higher by up to 589% (Blantyre) and 1020% (São Paulo), during windows-open and higher by up to 385% (São Paulo) and 390% (São Paulo) during fan-on, respectively. Coarse particles dominated the PM fraction during windows-open while fine particles dominated during fan-on and recirculation, indicating filter effectiveness in removing coarse particles and a need for filters that limit the ingress of fine particles. Spatial variation analysis during windows-open showed that pollution hotspots make up to a third of the total route-length. PM2.5 exposure for windows-open during off-peak hours was 91% and 40% less than morning and evening peak hours, respectively. Across cities, determinants of relatively high personal exposure doses included lower car speeds, temporally longer journeys, and higher in-car concentrations. It was also concluded that car-users in the least affluent cities experienced disproportionately higher in-car PM2.5 exposures. Cities were classified into three groups according to low, intermediate and high levels of PM exposure to car commuters, allowing to draw similarities and highlight best practices.
A series of experiments was undertaken on an intercity train carriage aimed at providing a “proof of concept” for three methods in improving our understanding of airflow behaviour and the accompanied dispersion of exhaled droplets. The methods used included the following: measuring CO2 concentrations as a proxy for exhaled breath, measuring the concentrations of different size fractions of aerosol particles released from a nebuliser, and visualising the flow patterns at cross-sections of the carriage by using a fog machine and lasers. Each experiment succeeded in providing practical insights into the risk of airborne transmission. For example, it was shown that the carriage is not well mixed over its length, however, it is likely to be well mixed along its height and width. A discussion of the suitability of the fresh air supply rates on UK train carriages is also provided, drawing on the CO2 concentrations measured during these experiments,