Journal of Hazardous, Toxic, and Radioactive Waste

The aim of this study was to assess the levels of metals in particulate matter measuring less than 10 μm (${\mathrm{PM}}_{10}$) aerosols in ambient air at a site in central Delhi. A total of seven metals were analyzed in ${\mathrm{PM}}_{10}$ aerosol samples during the postmonsoon period in 2009. The average levels of metals were estimated in the order $\mathrm{K}>\mathrm{Zn}>\mathrm{Mg}>\mathrm{Fe}>\mathrm{Mn}>\mathrm{Cu}>\mathrm{Cd}$. The study also covered the metal accumulation due to fireworks on Diwali festival. A comparison of ambient levels of metal accumulation with the Diwali period revealed that the festival Diwali is a source of severe air pollution when the levels of metals increased by many folds as compared with normal ambient levels. Significant amounts of metals are emitted into the atmosphere by burning of crackers and sparkles on the Diwali day (DD). The contribution of K was the highest during all DDs with its maximum 88% emission on the main DD. Remarkable increase was noticed for K (9.4 times) and Mg (6.1 times) on the DD over the pre-Diwali normal day, whereas for the pre-DD an increase of 6.2 and 4 times was noticed, respectively. Source apportionment using principal-component analysis revealed that crustal, industrial, and vehicular sources mainly contributed the metals in air during normal period, whereas fireworks and vehicular sources were identified as main contributors of metals during the Diwali period.

The results of a study on commuter exposure to carbon monoxide (CO) and ${\mathrm{PM}}_{10}$ during transit in the several commonly popular modes of transport in Mumbai, India, and Newcastle, U.K., are presented in this paper. In Mumbai, real-time exposure concentrations were measured while commuting along a route by bus, train, air-conditioned taxi, and a non-air-conditioned private car. In Newcastle, real-time exposure concentrations were measured while traveling by electric vehicle, public bus, and bicycle along a route. Average heart rate while commuting was monitored and subsequently simulated in a submaximal exercise test to give minute ventilation (VE) associated with each transport mode. The study in Mumbai has indicated that commuters traveling on buses and private non-air-conditioned cars are exposed to very high levels of air pollution compared with the train commuters. In Newcastle, electric vehicles and bicycles displayed the lowest exposure concentrations relative to buses. Higher exposure concentrations were observed for all travel modes in Mumbai as compared with Newcastle. When the inhaled amount and the lung deposition of pollutants are considered, cyclists are exposed to a higher amount of exposure dose in Newcastle because of the considerable increase in VE induced by the physical exertion of cycling. Thus, inhaled quantity and pollutant dosage give a better indication of the possible health risks and should be considered in future studies and in designing the cycle routes in cities.

Land application of biosolids to improve the fertility of agricultural land is very common in the United States. Bioaerosols are airborne particulate matter (PM) aerosolized during various agricultural processes such as preapplication preparation, disking, application, and postapplication management. In recent years, there has been concern about the health of people living near the application sites even though these processes have been going on for decades. The focus of this study is to characterize PM inside households located near fields where biosolids have been applied. The PM was collected inside five houses on eight polytetrafluoroethylene (PTFE) filter papers using the GRIMM 1.108 dust monitor. Physical characteristics of PM were analyzed using the scanning electron microscope (SEM). The morphological properties of particles such as diameter, area, perimeter, aspect ratio, and shape factor of particles were determined using Image J software for the images captured using SEM. It was observed that there were a majority of particles in the range of 2.5–5 μm on all filter papers except two. The average ${\mathrm{PM}}_{2.5}$ over eight filters was observed to be about 32%, while for the ranges of 2.5–5.0 and 5.0–10.0 μm the averages was about 38 and 20%, respectively. The PM 2.5 averages for smoking, nonsmoking, control houses, and study houses were observed to be 40.63, 28.86, 33.37, and 31.28%, respectively. The shape-factor distribution and shape-distribution results indicate that there were a higher percentage of agglomerates, which was followed by spherical and pentagonal shaped particles. It was estimated that on an average 37.79% of PM collected over all the eight filters were agglomerates while 28.28% were spherical in shape.

Megacities in India (population more than 10 million) have taken actions to control air-pollution emissions. However, the second-level cities (population between one and 10 million) have not drawn an action plan, and people face serious air pollution in these cities. For one such city, Lucknow, a geographic information system (GIS)-based methodology for emission inventory of on-road vehicles has been developed. The pollutants include: sulfur dioxide (${\mathrm{SO}}_2$); oxides of nitrogen (${\mathrm{NO}}_x$); carbon monoxide (CO); particulate matter (PM); 1,3 butadiene; formaldehyde; acetaldehyde; total aldehydes; and total polycyclic aromatic hydrocarbons (PAHs). Video recording was done at nine road intersections of varying land-use patterns to assess traffic count and vehicle kilometer travel. Parking lot surveys were carried out for assessing engine type, vehicle age, etc. and to arrive at a suitable net emission factor for each vehicle category. The 2-wheelers (2-Ws) and 4-wheelers (4-Ws) dominate the total traffic with an 83% share and are main sources of ${\mathrm{NO}}_x$ (46%) and CO (77%). The heavy duty vehicles (HDVs: buses and trucks), although they account for only 2% of the vehicle fleet, emit disproportionately high emissions (23% of ${\mathrm{SO}}_2$, 36% of ${\mathrm{NO}}_x$, and 28% of PM). Spatial cell ($2\times 2\mathrm{km}$)-wise emission inventory of pollutants indicates that the city center has the highest pollutant emissions resulting from a large number of vehicles, mostly 2-Ws, 3-Ws, and passenger cars. The inventory information can be used for short-term and long-term planning to reduce emissions.

The present study attempts to predict hourly ozone (${\mathrm{O}}_3$) and oxides of nitrogen (${\mathrm{NO}}_{\mathrm{x}}$) concentrations near a traffic intersection in megacity Delhi, India, using artificial neural network (ANN) with the Levenberg–Maquardt (LM) algorithm and decision tree algorithms [e.g., reduced error pruning tree (REPTree) and M5 P tree model]. The hourly averages of input variables of meteorological, traffic volume, and transport emissions along with target values of monitored ambient air concentrations of ${\mathrm{O}}_3$ and oxides of nitrogen ${\mathrm{NO}}_{\mathrm{x}}$ were used for model development. The LM, REPTree, and M5 P algorithm models were developed by training, validation, and testing of input and target data. Statistical agreement between observed and predicted values is assessed by coefficient of correlation (CC), mean square error (MSE), root mean square error (RMSE), normalized mean square Error (NMSE), and Nash–Sutcliffe efficiency index (N-S Index). Results show that the performance of the M5 P model is superior to ANN and REPTree models studied for prediction of ${\mathrm{O}}_3$ and ${\mathrm{NO}}_{\mathrm{x}}$ at a highly urbanized traffic intersection.

Gaseous toxic air pollutants are normally associated with acute short-term and chronic long-term health effects, and hence measurement of these pollutants is essential on a daily basis to understand the science of their occurrences. In this study, gaseous ${\mathrm{O}}_3$, ${\mathrm{SO}}_2$, ${\mathrm{NO}}_2$, ${\mathrm{NH}}_3$, ${\mathrm{HNO}}_2$, ${\mathrm{HNO}}_3$, and HCl and water-soluble inorganic ions (${\mathrm{Na}}^{+}$, ${\mathrm{K}}^{+}$, ${\mathrm{Ca}}^{2+}$, ${\mathrm{Mg}}^{2+}$, ${\mathrm{NH}}_4^{+}$, ${\mathrm{NO}}_3^{-}$, ${\mathrm{SO}}_4^{2-}$, and ${\mathrm{Cl}}^{-}$) in ${\mathrm{PM}}_{2.5}$ were measured during a summer in Kanpur, India, using an annular denuder technique on a time resolution of 12 h. The denuder technique revealed that evaporation losses of ${\mathrm{NH}}_4^{+}$, ${\mathrm{Cl}}^{-}$, and ${\mathrm{NO}}_3^{-}$ from a Teflon filter and trapped in nylon filters were temperature sensitive. The evaporation losses were $17.8\pm 8.7\%$, $29.5\pm 19.3\%$, and $37.1\pm 25.9\%$ during daytime, and $10.2\pm 6.4\%$, $17.7\pm 10.1\%$, and $19.3\pm 12.2\%$ during nighttime for ${\mathrm{NH}}_4^{+}$, ${\mathrm{Cl}}^{-}$, and ${\mathrm{NO}}_3^{-}$, respectively. Overall, 24 h average concentrations of ${\mathrm{O}}_3$, ${\mathrm{SO}}_2$, ${\mathrm{NO}}_2$, ${\mathrm{NH}}_3$, ${\mathrm{HNO}}_2$, ${\mathrm{HNO}}_3$, and HCl were 49.6, 8.6, 34.2, 21.5, 3.6, 9.8, and $1.4{\mathrm{\mu gm}}^{-3}$, respectively; and 2.1, 1.5, 2.9, 1.1, 13.1, 12.9, 19.7, and $1.3{\mathrm{\mu gm}}^{-3}$ were observed for ${\mathrm{Na}}^{+}$, ${\mathrm{K}}^{+}$, ${\mathrm{Ca}}^{2+}$, ${\mathrm{Mg}}^{2+}$, ${\mathrm{NH}}_4^{+}$, ${\mathrm{NO}}_3^{-}$, ${\mathrm{SO}}_4^{2-}$, and ${\mathrm{Cl}}^{-}$, respectively, in ${\mathrm{PM}}_{2.5}$. Levels of ${\mathrm{O}}_3$, ${\mathrm{NH}}_3$, ${\mathrm{HNO}}_2$, ${\mathrm{HNO}}_3$, ${\mathrm{PM}}_{2.5}$, ${\mathrm{K}}^{+}$, ${\mathrm{Ca}}^{2+}$, ${\mathrm{Mg}}^{2+}$, ${\mathrm{NO}}_3^{-}$, and ${\mathrm{SO}}_4^{2-}$ showed significant diurnal variations at 95% confidence level. However, ${\mathrm{SO}}_2$, ${\mathrm{NO}}_2$, HCl, ${\mathrm{Na}}^{+}$, ${\mathrm{NH}}_4^{+}$, and ${\mathrm{Cl}}^{-}$ did not show any significant diurnal variations. Ion balance showed that an abundant quantity of ${\mathrm{NH}}_3$ was present in the study area to neutralize ${\mathrm{H}}_2{\mathrm{SO}}_4$, ${\mathrm{HNO}}_3$, and HCl to form ${({\mathrm{NH}}_4)}_2{\mathrm{SO}}_4$, ${\mathrm{NH}}_4{\mathrm{NO}}_3$, and ${\mathrm{NH}}_4\mathrm{Cl}$, and thermodynamics for the formation of ${\mathrm{NH}}_4{\mathrm{NO}}_3$ was favored under more humid conditions.

Air pollution is an emerging public health concern as there are increasing evidences that the quality of air adversely affects human health due to the presence of various toxic pollutants. Linking air pollution from its source to adverse human health effects is a complicated phenomenon that requires a multidisciplinary approach for better understanding. Decision-makers need relevant, comprehensive estimates of the disease burden attributable to different risk factors. Many statistical models have become very relevant for estimating atmospheric concentrations by analysis of complex datasets to produce inferences and predictions that can lead to better management of air pollution. This paper focuses on the Indian scenario as a case study and presents the current status of air quality in India with special reference to particulate matter. The study suggest that air-quality networks need to be developed that can depict and forecast pollution levels with health advisories for public and for pollution emergencies measures. Development of statistical models, and methods for Big Data Analytics, can yield a wide array of actionable insights to facilitate policy decisions. Models may also be used to predict the cost of the air-pollution control measures as well as the benefits in terms of the control of acute and chronic diseases caused by air pollution. This study concludes that the application of statistical models and algorithms can act as an important tool to bridge the gap between science and policy.

The free-fall dust in ambient atmosphere provides important information about the nature and sources of pollutants in urban settings, because it incorporates the particles settling under gravity influence irrespective of their size. In the present work, free-fall dust samples were collected at a 1.5- and 9 m (5- and 30-ft) height at different locations to understand the impact of immediate and ultimate pollution sources, respectively, in Delhi. Samples collected near traffic intersections exhibited vehicular emissions; resuspended road side dust; and carbon and trace metals, such as Zn, Cu, Pb, Ni, Cr, and Cd. Interestingly, the total carbon fraction contributed more than 10% of the total collected mass at all sites. Zn and Ba concentrations dominated over other trace metals in samples at both heights and for all the sites. The authors’ results suggest that the presence of carbon in higher quantities has caused a dilution effect on trace metal concentrations. The study confirmed that crustally derived materials dominated the total mass of free-fall dust at both heights. Also, a statistical analysis (ANOVA) was also performed to find out the differences in samples from the two heights. The ionic composition (${\mathrm{F}}^{-}$, ${\mathrm{SO}}_4^{2-}$, ${\mathrm{NO}}_3^{-}$, and ${\mathrm{Cl}}^{-}$) of free-fall dust was also assessed. A significant correlation of total carbon with anions indicated combustion (fossil fuel and biomass) activities are the dominant sources of carbon and influence the chemistry of free-fall dust. The multivariate analysis of the data indicated three prevalent sources of analyzed chemical species: (1) construction activities, (2) resuspension of surface dust/soil, and (3) the fossil fuel and biomass combustion.

This paper presents the impact of air pollutants on human health and vegetation at selected industrial and control areas of India. Air quality data monitored by regulatory agency and health data obtained from local hospitals for the period 2010–2012 were analyzed. The particulate matter (PM) size distributions and lung function of the exposed people were also measured in the two study regions using a nonviable cascade impactor and spirometer, respectively. Results indicated that the 24-h average ${\mathrm{PM}}_{10}$ concentration at the industrial area was $125.03\pm 12.9\mathrm{\mu g}/{\mathrm{m}}^3$ and the measured ${\mathrm{PM}}_{2.5}$ and ${\mathrm{PM}}_{10}$ concentrations were $49.62\pm 5.41$ and $145.39\pm 9.44\mathrm{\mu g}/{\mathrm{m}}^3$, respectively. The measured ${\mathrm{PM}}_{10}$ and ${\mathrm{PM}}_{2.5}$ concentrations at the control area were $26.09\pm 0.38$ and $8.29\pm 0.18\mathrm{\mu g}/{\mathrm{m}}^3$, respectively. The health data recorded at hospitals indicated maximum hospital admissions during winter period. The lung function analysis for both the study areas revealed that the people residing in the industrial area were having a lower air exchange rate compared to the people residing in the control area. The effect of air quality on vegetation was also investigated. It was found that the plants’ photosynthetic rate in the industrial area was 50% lower than in the control area. The air pollution tolerance index (APTI) indicated highest APTI values of 13.1 and 16.4 for Bougainvillea plants in industrial and control areas, respectively. The lowest APTI value of 5.7 was observed for Eucalyptus species in the industrial area, which can be used as a potential bioindicator for air pollution assessment at the industrial area. Further, the dust deposition on the leaves of Bougainvillea was found to be 0.151 and $0.0989\mathrm{mg}/{\mathrm{cm}}^2$ at industrial and control areas, respectively.

Air pollution in megacities have caught attention of both researchers and policymakers because of increasing emissions, poor air quality, and potential adverse health impacts on densely inhabited populations. Oxides of nitrogen, particulate matter, carbon monoxide, and hydrocarbons are the major air pollutants of vehicular emissions near major intersections and arterials in megacities. The present study is mainly aimed at predicting ${\mathrm{PM}}_{2.5}$ and CO concentrations at an income tax office (ITO) intersection in the megacity of Delhi. Artificial neural networks (ANNs) and decision tree algorithms (e.g., REPTree and M5P algorithm techniques) are used to predict hourly fine particulate matter (${\mathrm{PM}}_{2.5}$) and carbon monoxide (CO) pollutant concentrations at the ITO intersection. Factors and parameters, such as meteorological conditions, traffic, and vehicular emissions, that affect pollutant concentrations are used in different combinations for the model development. Performance evaluation of ANN, REPTree, and M5P algorithms for hourly ${\mathrm{PM}}_{2.5}$ and CO concentration prediction is carried out, and the effects of the aforementioned factors are discussed. The M5P algorithm performs better than ANN and REPTree algorithms in that it precisely captures the relationships among the predictor variables and pollutant concentrations.