Search

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 (PM 2.5 and PM 10 ) 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 PM 2.5 (PM 10 ) 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 PM 2.5 and PM 10 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 PM 2.5 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 m 3 ; 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., (Copyright © 2022 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

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; PM 2.5 ) 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 PM 2.5 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 PM 2.5 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 PM 2.5 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 PM 2.5 exposure highlights the importance of implementing plausible solutions to make a positive impact on peoples' lives in these cities., (Copyright © 2021 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Countries around the world introduced strict restrictions on movement and activities known as 'lockdowns' to restrict the spread of the novel coronavirus disease (COVID-19) from the end of 2019. A sudden improvement in air quality was observed globally as a result of these lockdowns. To provide insight into the changes in air pollution levels in response to the COVID-19 restrictions we have compared surface air quality data in Delhi during four phases of lockdown and the first phase of the restriction easing period (25 March to 30 June 2020) with data from a baseline period (2018-2019). Simultaneously, short-term exposure of PM 2.5 and O 3 attributed premature mortality were calculated to understand the health benefit of the change in air quality. Ground-level observations in Delhi showed that concentrations of PM 10 , PM 2.5 and NO 2 dropped substantially in 2020 during the overall study period compared with the same period in previous years, with average reductions of ~49%, ~39%, and ~39%, respectively. An overall lower reduction in O 3 of ~19% was observed for Delhi. A slight increase in O 3 was found in Delhi's industrial and traffic regions. The highest peak of the diurnal variation decreased substantially for all the pollutants at every phase. The decrease in PM 2.5 and O 3 concentrations in 2020, prevented 904 total premature deaths, a 60% improvement when compared to the figures for 2018-2019. The restrictions on human activities during the lockdown have reduced anthropogenic emissions and subsequently improved air quality and human health in one of the most polluted cities in the world. Implications : I am submitting herewith the manuscript entitled "Unprecedented Reduction in Air Pollution and Corresponding Short-term Premature Mortality Associated with COVID-19 Forced Confinement in Delhi, India" for potential publishing in your journal.The novelty of this research lies in: (1) we utilized ground-level air quality data in Delhi during four phases of lockdown and the first phase of unlocking period (25 th March to 30 th June) for 2020 as well as data from the baseline period (2018-2019) to provide an early insight into the changes in air pollution levels in response to the COVID-19 pandemic, (2) Chatarize the change of diurnal variation of the pollutants and (3) we assess the health risk due to PM 2.5 and O 3 . Results from ground-level observations in Delhi showed that concentrations of PM 10 , PM 2.5 and NO 2 substantially dropped in 2020 during the overall study period compared to the similar period in previous years, with an average reduction of ~49%, ~39%, and ~39%, respectively. In the case of O 3 , the overall reduction was observed as ~19% in Delhi, while a slight increase was found in industrial and traffic regions. And consequently, the highest peak of the diurnal variation decreased substantially for all the pollutants. The health impact assessment of the changes in air quality indicated that 904 short-term premature deaths (~60%) were prevented due to the decline in PM 2.5 and O 3 concentrations in the study period. The restrictions on human activities during the lockdown have reduced the anthropogenic emissions and subsequently improved air quality and human health in one of the most polluted cities in the world.

Although lockdown of the industrial and transport sector and stay at home advisories to counter the COVID-19 pandemic have shown that the air quality has improved during this time, very little is known about the role of ambient air pollutants and meteorology in facilitating its transmission. This paper presents the findings from a study that was conducted to evaluate whether air quality index (AQI), three primary pollutants (PM 2.5 , PM 10 and CO), Ground level ozone (O 3 ) and three meteorological variables (temperature, relative humidity, wind speed) have promoted the COVID-19 transmission in five megacities of India. The results show significant correlation of PM 2.5 , PM 10 , CO, O 3 concentrations, AQI and meteorological parameters with the confirmed cases and deaths during the lockdown period. Among the meteorological variables considered, temperature strongly correlated with the COVID-19 cases and deaths during the lockdown (r=0.54;0.25) and unlock period (r=0.66;0.25). Among the pollutants, ozone, and among the meteorological variables, temperature, explained the highest variability, up to 34% and 30% respectively, for COVID-19 confirmed cases and deaths. AQI was not a significant parameter for explaining the variations in confirmed and death cases. WS and RH could explain 10-11% and 4-6% variations of COVID-19 cases. A GLM model could explain 74% and 35% variability for confirmed cases and deaths during the lockdown and 66% and 19% variability during the unlock period. The results suggest that meteorological parameters may have promoted the COVID-19 incidences, especially the confirmed cases. Our findings may encourage future studies to explore more about the role of ambient air pollutants and meteorology on transmission of COVID-19 and similar infectious diseases., (Copyright © 2021 Elsevier Inc. All rights reserved.)

This study analyzes the air pollution characteristics and their relation to meteorological conditions in Chennai, India. Meteorological conditions were the primary factor determining variations in daily average pollutant concentrations. The influence of urban infrastructure on meteorology is an important prediction on air quality. Understanding of the seasonal and diurnal secondary pollutant concentrations as a function of local meteorological conditions is necessary for urban air quality management. Micro-scale models for analyzing the surface layer interactions with the surrounding environment have recently gained attention. An attempt has been made to understand the effect of meteorology on air quality. This comprehensive study aims to assess the influence of local meteorology on urban air quality. The correlation was established between the change in meteorological parameters and mixing height on air quality at selected locations in a tropical urban environment. Results indicated the significant impact of land use patterns on the dispersion of air quality at study locations. Seasonal variations of ambient air temperatures at study locations were found to be more than 3°C in summer. Average mixing height variation among the study locations was observed to be more than 200 meters in summer. Results indicated the importance of wind velocity on the mixing height at study locations. The average concentrations of air quality parameters showed significant variation among the study locations. The maximum ozone (O 3 ) concentration was recorded at the Central Business District (CBD) during the afternoon, i.e., around 38.3 ppb, whereas it was 26.8 and 14.6 ppb at the Residential Area (RA) and Urban Baseline (UBL), respectively. A strong correlation was observed between ambient temperature and O 3 concentration during summer. In the winter, the average O 3 concentration in all three-study locations increased to 45.3 ppb, 45.8 ppb, and 58.5 ppb at UBL, RA, and CBD sites, respectively. The study reveals the impact of microenvironments on air quality. Implications : An attempt has been made to study the seasonal and diurnal variation of air quality levels in selected study regions with land cover change. This article focuses mainly on the surface temperature intensity variations with respect to the percentage of land use pattern change in Chennai city, India, and the subsequent effect on meteorology of dispersion conditions and air quality parameters has been studied. The relationship between local meteorology and air quality has been established.

The ground-level ozone (O 3 ) concentration in the urban regions of China has become an increasingly noticeable environmental problem in recent years. Many epidemiological studies have reported the association between O 3 pollution and mortality, only a few studies have focused on the O 3 -related mortality and corresponding economic effects at the Chinese city and province level. This study reports the seasonal variation of ground-level O 3 in 338 cities of China during the year 2016 and evaluates its effect on premature mortality and economic loss. It further illustrates the differences in cause-specific mortality outcomes of the log-linear and linear model, two of the prominently used methods for estimating health effects. In 2016, the annual average daily maximum 8-h O 3 concentration in China ranged between 74 and 201 μg/m 3 (138 ± 24.7 μg/m 3 ). 30% of the total population was exposed to >160 μg/m 3 O 3 concentration (Chinese national ambient air quality standard) and about 67.2% urban population lived in exposure above the WHO recommended O 3 concentrations (100 μg/m 3 ). The estimated national O 3 -attributable mortality was 74.2 × 10 3 (95% CI: 16.7×10 3 -127×10 3 ) in the log-linear model, whereas, the total O 3 -related mortality using the linear model was 69.6 × 10 3 (95% CI: 16.2 × 10 3 -115 × 10 3 ). The exposure to O 3 caused a nationwide economic loss of about 7.6 billion US$ (range: 1.7-12.9) in 2016. This study uniquely provides most comprehensive coverage of the Chinese cities for O 3 associated mortality utilizing ground level measurement data for 2016 and presents a measurable assessment to the policymakers of China for streamlining their efforts on air quality improvement and O 3 containment., (Copyright © 2019 Elsevier Ltd. All rights reserved.)

Open municipal solid waste (MSW) dumpsites are nowadays looming hotspots for water, air, and land pollution. Fresh and old MSW samples collected from a dumpsite in the coastal city of India were analyzed for moisture content, volatile content, energy content, elements, and toxic heavy metals. The compositional analysis results showed that fresh MSW consisted of 36% by weight bio-waste (food waste, yard waste, coconut waste) and around 30% recyclable materials (plastics, paper, cardboard, and metals). Approximately, 62% of the total fresh MSW was found to be combustible materials (plastics, paper, textile, rubber, cardboard, yard waste, and coconut husks). The analysis of old MSW samples collected from different depths (3-4 m and 6-7 m) showed the dominance of plastics (25-33%) and mixed residue (28-55%) having high energy content. Measurements of gaseous emission below 6-7 m from the surface indicated a higher concentration of methane (CH 4 :5.85 ± 0.12%) and lower concentration of carbon monoxide (CO: 3.82 ± 1.3 ppm), and hydrogen sulfide (H 2 S:10.15 ± 2.2 ppm). Haphazard dumping, waste characteristics, waste pile compaction processes and heat propagation due to deliberate fire may stimulate spontaneous fires., (Copyright © 2018 Elsevier Ltd. All rights reserved.)

Particulate matter (PM) is crucial among six criteria air pollutants, and it is frequently associated with human morbidity and mortality. According to the aerodynamic diameter, PM is classified as coarse (PM 10 ) and fine (PM 2.5 ). PM with these smaller sizes can easily enter and get deposited in the human airways. This deposited PM fraction commences the development of respiratory diseases such as asthma, chronic obstructive pulmonary disease, and even cancer. Thus, the quantification of PM deposition and its clearance in the human airway are essential for evaluating health risks. This study aims to investigate the size-segregated PM (PM 10 , PM 2.5, and PM 1 ) deposition in human lungs. Size-segregated PM is collected using the Grimm portable environmental dust monitor during winter season near an arterial road located in Chennai city of Tamil Nadu state, India. Multiple-Path Particle Dosimetry (MPPD) Model version 3.04 is utilized for quantifying PM deposition. In MPPD, airway structures of infants (3 and 28 months), children (3, 8, 9 and 14 years) and adults (18 and 21 years) are considered for the study. The values of PM concentration, body orientation, breathing scenario, tidal volume, pause fraction, inspiration fraction, and breathing frequency are specified in the MPPD for quantifying PM depositions. Results showed that 8-year children and 28 months infant groups are recorded with maximum and minimum size-segregated PM deposition respectively. The coarse particles (PM 10 ) are primarily deposited in the head (55-95%) and tracheobronchial (3-44%) regions whereas fine particles (PM 2.5 and PM 1 ) depositions are observed maximum in the head (36-63%) and pulmonary (28.2-52.7%) regions. Except for the adult age group, PM 2.5 has the maximum deposition percentage in tracheobronchial and pulmonary regions. In the case of lobar depositions, lower lobes receive maximum deposition (66.4%) than the upper (27.2%) and middle lobes (6.4%). PM 2.5 dominated the deposition in all five lobes of infant, children, and adults. The clearance rate of deposited PM is high in the tracheobronchial region whereas it is low in the pulmonary region. This study also concludes that PM 2.5 is the important size fraction in lung deposition. Further, the study results can be used for human health risk assessments such as oxidative potential and toxicity of deposited PM., (Copyright © 2018 Elsevier Inc. All rights reserved.)

China is in a critical stage of ambient air quality management after global attention on pollution in its cities. Industrial development and urbanization have led to alarming levels of air pollution with serious health hazards in densely populated cities. The quantification of cause-specific PM 2.5 -related health impacts and corresponding economic loss estimation is crucial for control policies on ambient PM 2.5 levels. Based on ground-level direct measurements of PM 2.5 concentrations in 338 Chinese cities for the year 2016, this study estimates cause-specific mortality using integrated exposure-response (IER) model, non-linear power law (NLP) model and log-linear (LL) model followed by morbidity assessment using log-linear model. The willingness to pay (WTP) and cost of illness (COI) methods have been used for PM 2.5 -attributed economic loss assessment. In 2016 in China, the annual PM 2.5 concentration ranged between 10 and 157 μg/m 3 and 78.79% of the total population was exposed to >35 μg/m 3 PM 2.5 concentration. Subsequently, the national PM 2.5 -attributable mortality was 0.964 (95% CI: 0.447, 1.355) million (LL: 1.258 million and NPL: 0.770 million), about 9.98% of total reported deaths in China. Additionally, the total respiratory disease and cardiovascular disease-specific hospital admission morbidity were 0.605 million and 0.364 million. Estimated chronic bronchitis, asthma and emergency hospital admission morbidity were 0.986, 1.0 and 0.117 million respectively. Simultaneously, the PM 2.5 exposure caused the economic loss of 101.39 billion US$, which is 0.91% of the national GDP in 2016. This study, for the first time, highlights the discrepancies associated with the three commonly used methodologies applied for cause-specific mortality assessment. Mortality and morbidity results of this study would provide a measurable assessment of 338 cities to the provincial and national policymakers of China for intensifying their efforts on air quality improvement., (Copyright © 2018 Elsevier Ltd. All rights reserved.)

In the present study, personal exposure to fine particulate matter (particulate matter with an aerodynamic diameter <2.5 μm [PM 2.5 ]) concentrations in an urban hotspot (central business district [CBD]) was investigated. The PM monitoring campaigns were carried out at an urban hotspot from June to October 2015. The personal exposure monitoring was performed during three different time periods, i.e., morning (8 a.m.-9 a.m.), afternoon (12.30 p.m.-1.30 p.m.), and evening (4 p.m.-5 p.m.), to cover both the peak and lean hour activities of the CBD. The median PM 2.5 concentrations were 38.1, 34.9, and 40.4 µg/m 3 during the morning, afternoon, and evening hours on the weekends. During weekdays, the median PM 2.5 concentrations were 59.5, 29.6, and 36.6 µg/m 3 in the morning, afternoon, and evening hours, respectively. It was observed that the combined effect of traffic emissions, complex land use, and micrometeorological conditions created localized air pollution hotspots. Furthermore, the total PM 2.5 lung dose levels for an exposure duration of 1 hr were 8.7 ± 5.7 and 12.3 ± 5.2 µg at CBD during weekends and weekdays, respectively, as compared with 2.5 ± 0.8 µg at the urban background (UB). This study emphasizes the need for mobile measurement for short-term personal exposure assessment complementing the fixed air quality monitoring., Implications: Personal exposure monitoring at an urban hotspot indicated space and time variation in PM concentrations that is not captured by the fixed air quality monitoring networks. The short-term exposure to higher concentrations can have a significant impact on health that need to be considered for the health risk-based air quality management. The study emphasizes the need of hotspot-based monitoring complementing the already existing fixed air quality monitoring in urban areas. The personal exposure patterns at hotspots can provide additional insight into sustainable urban planning.