Health risk assessment of workers' exposure to BTEX and PM during refueling in an urban fuel station.

The proximity of fuel stations to the roads and the activities inside the station can contribute to PM and VOCs and impose health risks on station workers. The study presents the exposure and health risk assessment of the fuel station personnel to total volatile organic compounds (TVOCs) and particulate matter (PM) during refueling operations. TVOCs and PM monitoring were carried out at a fuel station in Chennai, India, for 1 week in March 2021, covering both weekdays and weekends. The health risks were assessed using EPA's health impact assessment methodology. Exposure to TVOCs (3177.39 ± 5450.32 µg/m3) exceeded the EPA standard of 5 µg/m3, by more than 500 times, peaking during refueling operations. The average concentrations of PM10, PM2.5, and PM1 were 76.55 ± 23.08 µg/m3, 41.81 ± 9 µg/m3, and 30.38 ± 7.56 µg/m3, respectively. The concentrations were observed to be high during morning and evening hours due to the increased traffic on the adjacent road and inside the fuel station. The synergistic health risks linked with long-term exposure to high concentrations of BTEX and PM were also estimated. At the fuel station, a significant contribution to the SOA formation potential was shown by toluene, followed by m-xylene, p-xylene, o-xylene, ethylbenzene, and benzene. Furthermore, the deposition of airborne particles in the workers' respiratory tract was calculated using the Multiple Path Particle Dosimetry model while considering the daily average exposure duration of 12 h. The results showed that 59% of PM10 particles were deposited in the head region, whereas 11% and 10% of PM2.5 and PM1 particles were deposited in the pulmonary region. Hence, the health risk assessment indicated no non-cancer risk of exposure to PM (hazard quotient = 0.13) to station personnel exposed regularly for 1 year. However, prolonged exposure to VOCs for more than 1 year can result in both carcinogenic and non-carcinogenic risk (hazard quotient = 0.045 and cancer risk > 10-6) in workers.

In-kitchen aerosol exposure in twelve cities across the globe.

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 âˆ¼ 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.

Potential health risks due to in-car aerosol exposure across ten global 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.

Association of air pollution and meteorological variables with COVID-19 incidence: Evidence from five megacities in India.

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 (PM2.5, PM10 and CO), Ground level ozone (O3) 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 PM2.5, PM10, CO, O3 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.

In-car particulate matter exposure across ten global 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.

Off-Resonance Photoacoustic Spectroscopy Technique for Multi-Gas Sensing in Biogas Plants.

An off-resonance broadband photoacoustic spectroscopy (PAS) technique with a supercontinuum laser (SCL) in the near-infrared range is demonstrated for biogas measurements with different biomass matrices. The PAS sensor system has been calibrated with known concentrations of methane (CH4), carbon dioxide (CO2), water vapor (H2O vapor), and hydrogen sulfide (H2S). A laboratory-scale bioreactor was set up to monitor CH4 and CO2 generation using the SCL-PA sensor system. The obtained results show the suitability of the PAS sensor to be employed in a fully operational large scale biogas plant for online monitoring. The periodic variations in concentration for CH4, CO2, and H2O vapor were monitored in a large scale cattle dung based biogas plant in real-time, and the operating ranges were measured to be around 50-65%, 34-48%, and 0-1%, respectively. The SCL-PA sensor was also employed at two different Sewage Treatment Plants in Chennai, Tamilnadu, India, where along with CH4 (60-63%), CO2 (34-38%), and H2O vapor (0.8-1%), trace concentration levels of H2S were found to be around 0.04-1%.

Off-resonant photoacoustic spectroscopy for analysis of multicomponent gas mixtures at high concentrations using broadband vibrational overtones of individual gas species.

The broadband photoacoustic spectroscopy (PAS) technique is proposed and demonstrated for measurement of CH4, CO2, and H2O vapor in the 1.6 to 2.0 µm wavelength region. The wide spectrum of a supercontinuum light source is used to cover broadband absorption bands of multiple gas species. This sensor works in the off-resonant frequency of the designed photoacoustic cell and exhibits a wide concentration measurement range of parts per billion by volume (ppb-v) to 100%. The PAS sensor is further tested in real time by measuring the concentration of CO2, CH4, and H2O vapor in biogas plants.

Environmental burden by an open dumpsite in urban India.

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 (CH4:5.85 ±â€¯0.12%) and lower concentration of carbon monoxide (CO: 3.82 ±â€¯1.3 ppm), and hydrogen sulfide (H2S:10.15 ±â€¯2.2 ppm). Haphazard dumping, waste characteristics, waste pile compaction processes and heat propagation due to deliberate fire may stimulate spontaneous fires.

Characteristics of indoor air pollution and estimation of respiratory dosage under varied fuel-type and kitchen-type in the rural areas of Telangana state in India.

Indoor Air Pollution (IAP) is one of the top environmental risks in developing countries including India, with more than a million deaths annually, predominantly through Particulate Matter (PM) exposure. The current study deals with the measurement of PM concentrations in rural households under varied fuel and kitchen-types, evaluation of the indoor air pollution (IAP) characteristics and estimation of respiratory dosage for the different subjects (women, young children and the elderly). Monitoring of particulate matter (PM) was carried out during summer, monsoon and winter season with biomass, LPG and combine of biomass and LPG being used as fuel for cooking. Furthermore, different types of indoor kitchens (with partition and without partition) and outdoor kitchens (separate enclose kitchen and open kitchen) were also considered as kitchen type along with fuel are two crucial factors contributing to IAP. Deposition fractions were calculated using Multiple Particle Path Dosimetry (MPPD) to study the deposition patterns in different parts of the human respiratory tract (HRT) - head, tracheobronchial and pulmonary for women, young children and the elderly people. Dosage of particulate matter was calculated by inputting the recorded PM measurements, a comparison made for biomass-LPG and dosage intensification due to the kitchen-type presented. While the biomass households exhibited high levels of dosage (1181.4 to 5891.7 µg) against the LPG households (89.9 to 811.2 µg), the indoor kitchen types exhibited a maximum intensification of 10.6 times than outdoor kitchens with the same fuel. This study not only establishes the IAP characteristics but also quantifies the role of fuel-type and kitchen-type in IAP. The study also indicates various measures that could be deployed to reduce dosage and thus minimize the health risks.

Application of multiple-path particle dosimetry model for quantifying age specified deposition of particulate matter in human airway.

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 (PM10) and fine (PM2.5). 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 (PM10, PM2.5, and PM1) 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 (PM10) are primarily deposited in the head (55-95%) and tracheobronchial (3-44%) regions whereas fine particles (PM2.5 and PM1) depositions are observed maximum in the head (36-63%) and pulmonary (28.2-52.7%) regions. Except for the adult age group, PM2.5 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%). PM2.5 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 PM2.5 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.

PM2.5-related health and economic loss assessment for 338 Chinese cities.

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 PM2.5-related health impacts and corresponding economic loss estimation is crucial for control policies on ambient PM2.5 levels. Based on ground-level direct measurements of PM2.5 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 PM2.5-attributed economic loss assessment. In 2016 in China, the annual PM2.5 concentration ranged between 10 and 157 µg/m3 and 78.79% of the total population was exposed to >35 µg/m3 PM2.5 concentration. Subsequently, the national PM2.5-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 PM2.5 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.

Source characterization of PM10 and PM2.5 mass using a chemical mass balance model at urban roadside.

The 24-h average ambient particulate matter (PM(10) and PM(2.5)) concentrations are sampled concurrently during November 2008-April 2009 at a busy roadside in Chennai City, India. The elemental (Ag, Al, As, B, Ba, Be, Bi, Ca, Cd, Co, Cr, Cu, Fe, Ga, K, Li, Mg, Mn, Mo, Na, Ni, Pb, Rb, Se, Sr, Te, Tl, V and Zn) and ionic (Na(+), NH(4)(+), K(+), Ca(2+), Mg(2+), F(-), Cl(-), NO(2)(-), NO(3)(-) and SO(4)(2-)) composition of PM(10) and PM(2.5) are determined using an inductively coupled plasma-optical emission spectrometer (ICP-OES) and an ion chromatograph (IC), respectively. The emission inventory at the study area is also carried out to identify the likely PM emission sources. The U.S. EPA's-CMB (chemical mass balance) version 8.2 is applied to identify the source contribution of ambient PM(10) and PM(2.5) concentrations at the study area. Results indicated that diesel exhausts (43-52% in PM(10) and 44-65% in PM(2.5)) and gasoline exhausts (6-16% in PM(10) and 3-8% in PM(2.5)) are found to be the major source contributors at the study site followed by the paved road dusts (PM(10)=PM(2.5)=0.-2.3%), brake lining dusts (0.1% in PM(10) and 0.2% in PM(2.5)), brake pad wear dusts (0.1% in PM(10) and 0.01% in PM(2.5)), marine aerosols (PM(10)=PM(2.5)=0.1%) and cooking (~0.8% in PM(10) and ~1.5% in PM(2.5)).

Application of positive matrix factorization in characterization of PM(10) and PM(2.5) emission sources at urban roadside.

The 24-h average coarse (PM(10)) and fine (PM(2.5)) fraction of airborne particulate matter (PM) samples were collected for winter, summer and monsoon seasons during November 2008-April 2009 at an busy roadside in Chennai city, India. Results showed that the 24-h average ambient PM(10) and PM(2.5) concentrations were significantly higher in winter and monsoon seasons than in summer season. The 24-h average PM(10) concentration of weekdays was significantly higher (12-30%) than weekends of winter and monsoon seasons. On weekends, the PM(2.5) concentration was found to slightly higher (4-15%) in monsoon and summer seasons. The chemical composition of PM(10) and PM(2.5) masses showed a high concentration in winter followed by monsoon and summer seasons. The U.S.EPA-PMF (positive matrix factorization) version 3 was applied to identify the source contribution of ambient PM(10) and PM(2.5) concentrations at the study area. Results indicated that marine aerosol (40.4% in PM(10) and 21.5% in PM(2.5)) and secondary PM (22.9% in PM(10) and 42.1% in PM(2.5)) were found to be the major source contributors at the study site followed by the motor vehicles (16% in PM(10) and 6% in PM(2.5)), biomass burning (0.7% in PM(10) and 14% in PM(2.5)), tire and brake wear (4.1% in PM(10) and 5.4% in PM(2.5)), soil (3.4% in PM(10) and 4.3% in PM(2.5)) and other sources (12.7% in PM(10) and 6.8% in PM(2.5)).