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.

Chemical source profiles of fine particles for five different sources in Delhi.

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.

Regression-based flexible models for photochemical air pollutants in the national capital territory of megacity Delhi.

Modelling photochemical pollutants, such as ground level ozone (O3), nitric oxide (NO) and nitrogen dioxide (NO2), in urban terrain was proven to be cardinal, chronophagous and complex. We built linear regression and random forest regression models using 4-years (2015-2018; hourly-averaged) observations for forecasting O3, NO and NO2 levels for two scenarios (1-month prediction (for January 2019) and 1-year prediction (for 2019)) - with and without the impact of meteorology. These flexible models have been developed for, both, localised (site-specific models) and combined (indicative of city-level) cases. Both models were aided with machine learning, to reduce their time-intensity compared to models built over high-performance computing. O3 prediction performance of linear regression model at the city level, under both cases of meteorological consideration, was found to be significantly poor. However, the site-specific model with meteorology performed satisfactorily (r = 0.87; RK Puram site). Further, during testing, linear regression models (site-specific and combined) for NO and NO2 with meteorology, show a slight improvement in their prediction accuracies when compared to the corresponding equivalent linear models without meteorology. Random forest regression with meteorology performed satisfactorily for indicative city-level NO (r = 0.90), NO2 (r = 0.89) and O3 (r = 0.85). In both regression techniques, increased uncertainty in modelling O3 is attributed to it being a secondary pollutant, non-linear dependency on NOx, VOCs, CO, radicals, and micro-climatic meteorological parameters. Analysis of importance among various precursors and meteorology have also been computed. The study holistically concludes that site-specific models with meteorology perform satisfactorily for both linear regression and random forest regression.

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.

Urban local air quality management framework for non-attainment areas in Indian cities.

Increasing urban air pollution level in Indian cities is one of the major concerns for policy makers due to its impact on public health. The growth in population and increase in associated motorised road transport demand is one of the major causes of increasing air pollution in most urban areas along with other sources e.g., road dust, construction dust, biomass burning etc. The present study documents the development of an urban local air quality management (ULAQM) framework at urban hotspots (non-attainment area) and a pathway for the flow of information from goal setting to policy making. The ULAQM also includes assessment and management of air pollution episodic conditions at these hotspots, which currently available city/regional-scale air quality management plans do not address. The prediction of extreme pollutant concentrations using a hybrid model differentiates the ULAQM from other existing air quality management plans. The developed ULAQM framework has been applied and validated at one of the busiest traffic intersections in Delhi and Chennai cities. Various scenarios have been tested targeting the effective reductions in elevated levels of NOx and PM2.5 concentrations. The results indicate that a developed ULAQM framework is capable of providing an evidence-based graded action to reduce ambient pollution levels within the specified standard level at pre-identified locations. The ULAQM framework methodology is generalised and therefore can be applied to other non-attainment areas of the country.

Effect of PM2.5 chemical constituents on atmospheric visibility impairment.

PM2.5 sampling was conducted at a curbside location in Delhi city for summer and winter seasons, to evaluate the effect of PM2.5 and its chemical components on the visibility impairment. The PM2.5 concentrations were observed to be higher than the National Ambient Air Quality Standards (NAAQS), indicating poor air quality. The chemical constituents of PM2.5 (the water-soluble ionic species SO42-, NO3-, Cl-, and NH4+, and carbonaceous species: organic carbon, elemental carbon) were analyzed to study their impact on visibility impairment by reconstructing the light extinction coefficient, bext. The visibility was found to be negatively correlated with PM2.5 and its components. The reconstructed bext showed that organic matter was the largest contributor to bext in both the seasons which may be attributed to combustion sources. In summer season, it was followed by elemental carbon and ammonium sulfate; however, in winter, major contributions were from ammonium nitrate and elemental carbon. Higher elemental carbon in both seasons may be attributed to traffic sources, while lower concentrations of nitrate during summer, may be attributed to volatility because of higher atmospheric temperatures. IMPLICATIONS: The chemical constituents of PM2.5 that majorly effect the visibility impairment are organic matter and elemental carbon, both of which are products of combustion processes. Secondary formations that lead to ammonium sulfate and ammonium nitrate production also impair the visibility.

The influence of odd-even car trial on fine and coarse particles in Delhi.

The odd-even car trial scheme, which reduced car traffic between 08.00 and 20.00 h daily, was applied from 1 to 15 January 2016 (winter scheme, WS) and 15-30 April 2016 (summer scheme, SS). The daily average PM2.5 and PM10 exceeded national standards, with highest concentrations (313 µg m-3 and 639 µg m-3, respectively) during winter and lowest (53 µg m-3 and 130 µg m-3) during the monsoon (June-August). PM concentrations during the trials can be interpreted either as reduced or increased, depending on the periods used for comparison purposes. For example, hourly average net PM2.5 and PM10 (after subtracting the baseline concentrations) reduced by up to 74% during the majority (after 1100 h) of trial hours compared with the corresponding hours during the previous year. Conversely, daily average PM2.5 and PM10 were higher by up to 3-times during the trial periods when compared with the pre-trial days. A careful analysis of the data shows that the trials generated cleaner air for certain hours of the day but the persistence of overnight emissions from heavy goods vehicles into the morning odd-even hours (0800-1100 h) made them probably ineffective at this time. Any further trial will need to be planned very carefully if an effect due to traffic alone is to be differentiated from the larger effect caused by changes in meteorology and especially wind direction.

Radiation safety aspects of the operation of first three synchrotron beam lines of Indus-2.

Five synchrotron radiation beam lines are commissioned and now under regular operation at the Synchrotron Radiation Source, Indus-2 at Raja Ramanna Centre For Advanced Technology (RRCAT), Indore, India. Nine beam lines are under trial operation, and six beam lines are in the installation stage. In the early phase of installation of beam lines on Indus-2, three bending magnet beam lines, Extended X-ray Absorption Fine Structure (EXAFS, BL-8), Energy Dispersive X-ray Diffraction (EDXRD, BL-11) and Angle Dispersive X-ray Diffraction (ADXRD, BL-12), were installed and commissioned, after approval from Atomic Energy Regulatory Board (AERB), India. These beam lines are pink (BL-8), white (BL-11) and monochromatic (BL-12), which are housed in specially designed shielded hutches. In order to ensure safety of users and other working personnel from ionizing radiations present in these beam lines, several safety systems are incorporated and safety procedures are followed. The paper describes the radiological safety aspects of the three beam lines during its initial commissioning trials and also the measurements on radiation levels carried out in and around the beam line hutches.

Organic matter determination for street dust in Delhi.

The organic matter of street dust is considered as one of the causes for high human mortality rate. To understand the association, the street dust samples were collected from four different localities (industrial, residential, residential-commercial, and commercial) situated in the greater Delhi area of India. The loss-on-ignition method was used to determine the organic matter (OM) content in street dust. The OM content, potassium, calcium, sulfate, and nitrate concentrations of street dust in Delhi, India is measured to understand the spatial variation. Correlation analysis, analysis of variance, and factor analysis were performed to define the sources. The dust OM level ranges from 2.63 to 10.22 %. It is found through correlation and factor analysis that OM is primarily contributed from secondary aerosol and vehicular exhaust. The OM levels suggest that the use of a residential-commercial site for commercial purposes is polluting the street dust and creating the environmental and human health problems.

Indoor air quality modeling for PM 10, PM 2.5, and PM 1.0 in naturally ventilated classrooms of an urban Indian school building.

Assessment of indoor air quality (IAQ) in classrooms of school buildings is of prime concern due to its potential effects on student's health and performance as they spend a substantial amount of their time (6-7 h per day) in schools. A number of airborne contaminants may be present in urban school environment. However, respirable suspended particulate matter (RSPM) is of great significance as they may significantly affect occupants' health. The objectives of the present study are twofold, one, to measure the concentrations of PM(10) (<10 microm), PM(2.5) (<2.5 microm), and PM(1.0) (<1.0 microm) in naturally ventilated classrooms of a school building located near a heavy-traffic roadway (9,755 and 4,296 vehicles/hour during weekdays and weekends, respectively); and second, to develop single compartment mass balance-based IAQ models for PM(10) (NVIAQM(pm10)), PM(2.5) (NVIAQM(pm2.5)), and PM(1.0) (NVIAQM(pm1.0)) for predicting their indoor concentrations. Outdoor RSPM levels and classroom characteristics, such as size, occupancy level, temperature, relative humidity, and CO(2) concentrations have also been monitored during school hours. Predicted indoor PM(10) concentrations show poor correlations with observed indoor PM(10) concentrations (R (2) = 0.028 for weekdays, and 0.47 for weekends). However, a fair degree of agreement (d) has been found between observed and predicted concentrations, i.e., 0.42 for weekdays and 0.59 for weekends. Furthermore, NVIAQM(pm2.5) and NVIAQM(pm1.0) results show good correlations with observed concentrations of PM(2.5) (R(2) = 0.87 for weekdays and 0.9 for weekends) and PM(1.0) (R(2) = 0.86 for weekdays and 0.87 for weekends). NVIAQM(pm10) shows the tendency to underpredict indoor PM(10) concentrations during weekdays as it does not take into account the occupant's activities and its effects on the indoor concentrations during the class hours. Intense occupant's activities cause resuspension or delayed deposition of PM(10). The model results further suggests conductance of experimental and physical simulation studies on dispersion of particulates indoors to investigate their resuspension and settling behavior due to occupant's activities/movements. The models have been validated at three different classroom locations of the school site. Sensitivity analysis of the models has been performed by varying the values of mixing factor (k) and newly introduced parameter R(c). The results indicate that the change in values of k (0.33 to 1.00) does not significantly affect the model performance. However, change in value of R(c) (0.001 to 0.500) significantly affects the model performance.

Construction of fuzzy membership functions for urban vehicular exhaust emissions modeling.

This paper presents a method for constructing a membership function (MF) for the fuzzy sets that expert systems deal with. This paper introduces a Bezier curve-based mechanism for constructing MFs of convex normal fuzzy sets. The mechanism can fit any given data set with a minimum level of discrepancy. In the absence of data, the mechanism can be intuitively manipulated by the user to construct MFs with the desired shape. MFs have been developed using the proposed mechanism for urban vehicular exhaust emission modeling. It has been observed that all meteorological and vehicular parameters have either S-shaped MFs or Z-shaped MFs. Gaussian MF has been mostly applied for modeling air quality. The present study explored the application of fuzzy MF to analyze air pollution data from vehicular emission. The study reveals that S-shaped and Z-shaped MF can be used in addition to Gaussian MF.

Urban air quality in mega cities: a case study of Delhi City using vulnerability analysis.

Air pollution is one of the major environmental problems in India, affecting health of thousands of 'urban' residents residing in mega cities. The need of the day is to evolve an 'effective' and 'efficient' air quality management plan (AQMP) encompassing the essential 'key players' and 'stakeholders.' This paper describes the formulation of an AQMP for mega cities like Delhi in India taking into account the aforementioned key 'inputs.' The AQMP formulation methodology is based on past studies of Longhurst et al., (Atmospheric Environment, 30, 3975-3985, 1996); Longhurst & Elsom, ((1997). Air Pollution-II, Vol. 2 (pp. 525-532)) and Beatti et al., (Atmospheric Environment, 35, 1479-1490, 2001). Further, the vulnerability analysis (VA) has been carried out to evaluate the stresses due to air pollution in the study area. The VA has given the vulnerability index (VI) of 'medium to high' and 'low' at urban roadways/intersections and residential areas, respectively.

Artificial neural network based carbon monoxide persistence models for episodic urban air quality management.

This paper describes the development of artificial neural network (ANN) based carbon monoxide (CO) persistence (ANNCOP) models to forecast 8-h average CO concentration using 1-h maximum predicted CO data for the critical (winter) period (November-March). The models have been developed for three 8-h groupings of 10 P.M. to 6 A.M., 6 A.M., to 2 P.M. and 2-10 P.M., at two air quality control regions (AQCRs) in Delhi city, representing an urban intersection and an arterial road consisting heterogeneous traffic flows. The result indicates that time grouping of 2-10 PM is dominantly affected by inversion conditions and peak traffic flow. The ANNCOP model corresponding to this grouping predicts the 8-h average CO concentrations within the accuracy range of 68-71%. The CO persistence values derived from ANNCOP model are comparable with the persistence values as suggested by the Environmental Protection Agency (EPA), USA. This work demonstrates that ANN based model is capable of describing winter period CO persistence phenomena.

Suspended particulate matter distribution in rural-industrial Satna and in urban-industrial South Delhi.

An air quality sampling program was designed and implemented to collect the baseline concentrations of respirable suspended particulates (RSP = PM10), non-respirable suspended particulates (NRSP) and fine suspended particulates (FSP = PM2.5). Over a three-week period, a 24-h average concentrations were calculated from the samples collected at an industrial site in Southern Delhi and compared to datasets collected in Satna by Envirotech Limited, Okhla, Delhi in order to establish the characteristic difference in emission patterns. PM2.5, PM10, and total suspended particulates (TSP) concentrations at Satna were 20.5 +/- 6.0, 102.1 +/- 41.1, and 387.6 +/- 222.4 microg m(-3) and at Delhi were 126.7 +/- 28.6, 268.6 +/- 39.1, and 687.7 +/- 117.4 microg m(-3). Values at Delhi were well above the standard limit for 24-h PM2.5 United States National Ambient Air Quality Standards (USNAAQS; 65 microg m(-3)), while values at Satna were under the standard limit. Results were compared with various worldwide studies. These comparisons suggest an immediate need for the promulgation of new PM2.5 standards. The position of PM10 in Delhi is drastic and needs an immediate attention. PM10 levels at Delhi were also well above the standard limit for 24-h PM10 National Ambient Air Quality Standards (NAAQS; 150 microg m(-3)), while levels at Satna remained under the standard limit. PM2.5/PM10 values were also calculated to determine PM2.5 contribution. At Satna, PM2.5 contribution to PM10 was only 20% compared to 47% in Delhi. TSP values at Delhi were well above, while TSP values at Satna were under, the standard limit for 24-h TSP NAAQS (500 microg m(-3)). At Satna, the PM10 contribution to TSP was only 26% compared to 39% in Delhi. The correlation between PM10, PM2.5, and TSP were also calculated in order to gain an insight to their sources. Both in Satna and in Delhi, none of the sources was dominant a varied pattern of emissions was obtained, showing the presence of heterogeneous emission density and that nonrespirable suspended particulate (NRSP) formed the greatest part of the particulate load.