Rapid urbanization along with industrial growth is one of the major causes of elevated air pollution levels in urban areas of low and middle income countries (LMICs). They are further associated with ...adverse health impacts within urban ecosystems. In order to manage and control deteriorating urban air quality, an efficient and effective urban air quality management plan is required consisting of systematic sampling, monitoring and analysis; modelling; and control protocols. Air quality monitoring is the essential and basic step that develops foundation of any management plan. The present research article describes a comprehensive methodology for establishing a systematic and robust air quality monitoring network in LMICs and strengthening the effectiveness and efficiency of urban air quality management frameworks. It also describes step-by-step procedures for chemical characterization of both organic and inorganic constituents of ambient particulate matter along with molecular markers, which are essential to identify the corresponding sources of particulate matter, an integral part of air pollution monitoring protocol. Additionally, it discusses the need for coupling low cost wireless sensor-based stations with a limited number of manual and conventional real time ambient air monitoring stations in order to make it cost effective, yet robust. The article demonstrates that satellite-based remote sensing monitoring calibrated with ground level measurement has the potential for regional scale air quality monitoring that captures transport of transboundary pollution.
•Status of urban air pollution in LMIC.•Challenges and issues of air quality management in LMIC.•Air quality monitoring protocol in LMIC.•Source apportionment protocol in LMIC.
Fuel combustion-fossil fuel combustion in high-income and middle-income countries and burning of biomass in low-income countries-accounts for 85% of airborne particulate pollution and for almost all ...pollution by oxides of sulphur and nitrogen. ...ambient air pollution, chemical pollution, and soil pollution-the forms of pollution produced by industry, mining, electricity generation, mechanised agriculture, and petroleum-powered vehicles-are all on the rise, with the most marked increases in rapidly developing and industrialising low-income and middle-income countries. Pollution mitigation and prevention can yield large net gains both for human health and the economy. ...air quality improvements in the high-income countries have not only reduced deaths from cardiovascular and respiratory disease but have also yielded substantial economic gains. Pollution control, in turn, will benefit from efforts to slow the pace of climate change (SDG 13) by transitioning to a sustainable, circular economy that relies on non-polluting renewable energy, on efficient industrial processes that produce little waste, and on transport systems that restrict use of private vehicles in cities, enhance public transport, and promote active travel.
The association of air pollution with multiple adverse health outcomes is becoming well established, but its negative economic impact is less well appreciated. It is important to elucidate this ...impact for the states of India.
We estimated exposure to ambient particulate matter pollution, household air pollution, and ambient ozone pollution, and their attributable deaths and disability-adjusted life-years in every state of India as part of the Global Burden of Disease Study (GBD) 2019. We estimated the economic impact of air pollution as the cost of lost output due to premature deaths and morbidity attributable to air pollution for every state of India, using the cost-of-illness method.
1·67 million (95% uncertainty interval 1·42–1·92) deaths were attributable to air pollution in India in 2019, accounting for 17·8% (15·8–19·5) of the total deaths in the country. The majority of these deaths were from ambient particulate matter pollution (0·98 million 0·77–1·19) and household air pollution (0·61 million 0·39–0·86). The death rate due to household air pollution decreased by 64·2% (52·2–74·2) from 1990 to 2019, while that due to ambient particulate matter pollution increased by 115·3% (28·3–344·4) and that due to ambient ozone pollution increased by 139·2% (96·5–195·8). Lost output from premature deaths and morbidity attributable to air pollution accounted for economic losses of US$28·8 billion (21·4–37·4) and $8·0 billion (5·9–10·3), respectively, in India in 2019. This total loss of $36·8 billion (27·4–47·7) was 1·36% of India's gross domestic product (GDP). The economic loss as a proportion of the state GDP varied 3·2 times between the states, ranging from 0·67% (0·47–0·91) to 2·15% (1·60–2·77), and was highest in the low per-capita GDP states of Uttar Pradesh, Bihar, Rajasthan, Madhya Pradesh, and Chhattisgarh. Delhi had the highest per-capita economic loss due to air pollution, followed by Haryana in 2019, with 5·4 times variation across all states.
The high burden of death and disease due to air pollution and its associated substantial adverse economic impact from loss of output could impede India's aspiration to be a $5 trillion economy by 2024. Successful reduction of air pollution in India through state-specific strategies would lead to substantial benefits for both the health of the population and the economy.
UN Environment Programme; Bill & Melinda Gates Foundation; and Indian Council of Medical Research, Department of Health Research, Ministry of Health and Family Welfare, Government of India.
A study on indoor–outdoor RSPM (PM
10, PM
2.5 and PM
1.0) mass concentration monitoring has been carried out at a classroom of a naturally ventilated school building located near an urban roadway in ...Delhi City. The monitoring has been planned for a year starting from August 2006 till August 2007, including weekdays (Monday, Wednesday and Friday) and weekends (Saturday and Sunday) from 8:0 a.m. to 2:0 p.m., in order to take into account hourly, daily, weekly, monthly and seasonal variations in pollutant concentrations. Meteorological parameters, including temperature, rH, pressure, wind speed and direction, and traffic parameters, including its type and volume has been monitored simultaneously to relate the concentrations of indoor–outdoor RSPM with them. Ventilation rate has also been estimated to find out its relation with indoor particulate concentrations. The results of the study indicates that RSPM concentrations in classroom exceeds the permissible limits during all monitoring hours of weekdays and weekends in all seasons that may cause potential health hazards to occupants, when exposed. I/O for all sizes of particulates are greater than 1, which implies that building envelop does not provide protection from outdoor pollutants. Further, a significant influence of meteorological parameters, ventilation rate and of traffic has been observed on I/O. Higher I/O for PM
10 is indicating the presence of its indoor sources in classroom and their indoor concentrations are strongly influenced by activities of occupants during weekdays.
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•In-kitchen particulate matter (PM) exposure was measured across twelve major cities.•Charcoal-based cooking increased PM2.5 by ∼ 3.1-times compared with LPG.•Dual ...(mechanical + natural) ventilation reduced exposure by ∼ 2-times than natural only.•Irrespective of ventilation and fuel type, extensive frying resulted in highest exposure.•Hazard ratio was above the standard limit in 47 out of the 60 homes.
Poor ventilation and polluting cooking fuels in low-income homes cause high exposure, yet relevant global studies are limited. We assessed exposure to in-kitchen particulate matter (PM2.5 and PM10) employing similar instrumentation in 60 low-income homes across 12 cities: Dhaka (Bangladesh); Chennai (India); Nanjing (China); Medellín (Colombia); São Paulo (Brazil); Cairo (Egypt); Sulaymaniyah (Iraq); Addis Ababa (Ethiopia); Akure (Nigeria); Blantyre (Malawi); Dar-es-Salaam (Tanzania) and Nairobi (Kenya). Exposure profiles of kitchen occupants showed that fuel, kitchen volume, cooking type and ventilation were the most prominent factors affecting in-kitchen exposure. Different cuisines resulted in varying cooking durations and disproportional exposures. Occupants in Dhaka, Nanjing, Dar-es-Salaam and Nairobi spent > 40% of their cooking time frying (the highest particle emitting cooking activity) compared with ∼ 68% of time spent boiling/stewing in Cairo, Sulaymaniyah and Akure. The highest average PM2.5 (PM10) concentrations were in Dhaka 185 ± 48 (220 ± 58) μg m−3 owing to small kitchen volume, extensive frying and prolonged cooking compared with the lowest in Medellín 10 ± 3 (14 ± 2) μg m−3. Dual ventilation (mechanical and natural) in Chennai, Cairo and Sulaymaniyah reduced average in-kitchen PM2.5 and PM10 by 2.3- and 1.8-times compared with natural ventilation (open doors) in Addis Ababa, Dar-es-Salam and Nairobi. Using charcoal during cooking (Addis Ababa, Blantyre and Nairobi) increased PM2.5 levels by 1.3- and 3.1-times compared with using natural gas (Nanjing, Medellin and Cairo) and LPG (Chennai, Sao Paulo and Sulaymaniyah), respectively. Smaller-volume kitchens (<15 m3; Dhaka and Nanjing) increased cooking exposure compared with their larger-volume counterparts (Medellin, Cairo and Sulaymaniyah). Potential exposure doses were highest for Asian, followed by African, Middle-eastern and South American homes. We recommend increased cooking exhaust extraction, cleaner fuels, awareness on improved cooking practices and minimising passive occupancy in kitchens to mitigate harmful cooking emissions.
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•17–28% of time spent at hotspots contributed to 33–58% of total inhaled PM2.5 doses.•Higher GDP and VSL agreed with a decrease of hotspot percentage length.•No significant ...correlations were found between fuel price and in-car PM2.5 exposure.•Highest deaths, 2.46 per 100,000 car commuters per year, estimated in Dar-es-Salaam.•Economic losses were inversely proportional to per capita GDP in most cities.
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 ≤2.5 µm; 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.
This paper analyzes the statistical behavior of the ground level ozone concentrations (GLO) observed at a major traffic intersection in Delhi. Five sets of data, i.e. summer (May to July, high solar ...radiation data), winter (November to January, low solar radiation data), spring (March to April), autumn (September to October), and the entire year have been used to study the seasonal variation in the statistical behavior of GLO. Appropriate statistical distribution form has been identified from alternative candidate distribution models using the goodness-of-fit methods and parameters have been estimated using the method of maximum likelihood. The yearly, winters, spring, and summer datasets were found to follow the log-normal distribution model, while autumn dataset followed Weibull distribution. Analysis shows that ozone concentrations also show similar statistical behavior like other air pollutants and fit mainly to the log-normal distribution as reported for other pollutants in different studies. The seasonality of the datasets shows higher skewness during summers due to longish tail of the distribution mainly on account of higher photo–chemical activity. The probability density functions corresponding to the five datasets were used to compute the probability of exceedence of the National Ambient Air Quality Standards and return period of violation of standards. The distributions have also been used to classify the study region under various air quality descriptor categories. The region is found to violate the air quality compliance criteria 17% of the recorded times in the year. Alternative measures have been discussed to reduce the precursor emissions in order to achieve the air quality goals.
Chemical characterization and source apportionment of PM
10
and PM
2.5
were carried out for two different elevations (lower elevation (LE) ~ 5–10 m and higher elevation (HE) ~ 30–45 m) at four ...different locations representing urban background, city center, upwind, and downwind of the Delhi city during January 2017–March 2017. The 24-h average PM
10
and PM
2.5
concentrations were varied between 135.2–258.7 and 79.3–120.9 µg/m
3
, respectively. The average PM
10
and PM
2.5
concentrations were found significantly higher at LE than HE. The PM samples were analyzed for ions, elements and carbonaceous matter (EC/OC), and their concentrations (except S, V, As, Ni, Sb, Sr, Ga, elements associated with industrial combustion activities, and NO
3
−
, attributed to high nitrate formation potential at HE) were observed higher in LE than HE at all the study locations. The chemical mass balance model was applied to quantify the source contributions to PM
10
and PM
2.5
mass at two different elevations. Model identified vehicular emission (diesel, PM
10
~ 8.8–21.7% and PM
2.5
~ 10.5–24.4% and gasoline, PM
10
~ 4.8–15.6% and PM
2.5
~ 6.7–14.8%), industrial residual oil combustion (PM
10
~ 8.8–23.5% and PM
2.5
~ 3.2–10.4%), road dust (PM
10
~ 13.6–22.3% and PM
2.5
~ 8.8–17.8%), soil dust (PM
10
~ 33.8–41.1% and PM
2.5
~ 5.8–8.3%), secondary nitrate (PM
10
~ 6.1–16.2% and PM
2.5
~ 13.4–20.2%), secondary sulfate (PM
10
~ 7.1–12.3% and PM
2.5
~ 10.6–16.7%), and biomass burning (PM
10
~ 6.8–21.8% and PM
2.5
~ 4.9–38.7%) as the main sources of PM
10
and PM
2.5
mass at both the elevations at all the study sites. The contribution of industrial residual oil combustion, vehicular emission, and secondary nitrate to PM
10
and PM
2.5
mass was found relatively higher in HE than LE. Results also revealed that biomass burning contributed significantly to PM pollution in the outskirts of Delhi than inside the city. Further, potential source contribution function analysis revealed that there may not be a long-range transport of PM emitted from biomass burning in the upwind region of Delhi during the study period. Shifting to Indian BS VI vehicles and fuel, switching to cleaner fuel in slum households, strict compliance on industries, and regular vacuum cleaning of roads will reduce the severe air quality problem in Delhi.
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