Study of ground ozone and precursors along with particulate matter at residential sites in the vicinity of a power plant

doi:10.21203/rs.3.rs-2598571/v1

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Study of ground ozone and precursors along with particulate matter at residential sites in the vicinity of a power plant

https://doi.org/10.21203/rs.3.rs-2598571/v1

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Ground ozone (O₃) and its precursor variables carbon monoxide (CO), nitric oxide (NO), nitrogen dioxide (NO₂), Sulphur dioxide (SO₂), benzene, toluene, ethyl-benzene and xylene (BTEX) along with particulate matter of size less than 10 and 2.5 micron (PM₁₀and PM_2.5) and meteorological variables have been studied at a residential site near the coal-fired power plant in two cities; Chandrapur and Nagpur during 2016-2019. O₃ is observed to be not correlated significantly with any of its precursor variables. On a finer time scale however, a significant correlation of O₃ with CO, NO, NO₂ and BTEX suggested that O₃ formation mechanism is driven by VOCs (mainly BTEX), CO and NOx. On the coarser scale however seasonality and other factors have distorted the correlation. Random forest model with O₃ concentration as the response variable and NO₂, NO, SO₂, CO, BTEX, PM₁₀ and PM_2.5 as independent variables suggested that PM₁₀, NO, CO and solar radiation are highly important variable governing the O₃ dynamics in Chandrapur. In Nagpur, wind direction, relative humidity, temperature, toluene and NO₂ are more important. Qualitative analysis to assess the contribution of emission sources suggested the influence of traffic emissions in Nagpur and dominance of non-traffic related emissions, mainly power plant and mining activities in Chandrapur. Hazard index is observed to be > 1 in both the cities suggesting the health hazard to the residents living in the area.

Ground ozone

particulate matter

BTEX

power plant

hazard quotient

random forest model

Coal-fired power plants are notorious for causing harmful air pollution emissions which are responsible for human health hazard. Emissions from coal-based power plant comprise oxides of such as carbon monoxide (CO), carbon dioxide, greenhouse gases, sulphur dioxide (SO₂), nitrogen dioxide (NO₂), particulate matter (PM) of diameter less than 10 micron (PM₁₀) and diameter less than 2.5 micron (PM_2.5) along with their constituents and Volatile Organic Compounds (VOCs). These pollutants cause serious diseases such as respiratory ailments, cancer in various forms and mortality (Pope and Dockery, 2006; Guttikunda and Jawahar, 2014; Olaniyan et al., 2017; Pope et al., 2019). In India, emissions from power plants have caused 80 to 115 thousand premature deaths (Guttikunda and Jawahar, 2014). People living in the vicinity of coal-fired plants and coal mines are exposed to these pollutants (Olufemi et al., 2019; Wright et al., 2011; Liu et al., 2012). In a study on health impacts of coal-fired power plants on the people residing in a nearby and distant locality, the association between hospitalization rate for respiratory illness and residential proximity was observed (Liu et al., 2012). The exposure to PM, NOx and ozone pollution has led to respiratory and cardiovascular diseases in the exposed population (Alberini et al., 1997; Chhabra et al., 2001; Salam et al., 2005; Kenessary et al., 2019; Wang et al., 2020; Cohen et al., 2005). Air pollution in India has been identified as one of the major problems and it accounts for the 10% of the disease burden in the country (Indian Council of Medical Research et al., 2017).

Ground-level ozone (O₃), one of the major constituents of photochemical smog, is formed by the photochemical reaction of sunlight with oxides of nitrogen and volatile organic compounds (VOCs). Excessive ozone in ambient air can cause lung diseases, asthma and breathing problems. The health effects of precursors of ozone are established well in the literature e.g. Human health effects of exposure to NO₂ are stroke, bronchitis and reduced lung function (Andersen et al., 2012). Some of the VOCs are carcinogenic and the health impacts are both acute and chronic in nature. In addition to the health impacts such as headache, nausea, dizziness, allergic skin reactions, the other health impacts are damage to liver and kidneys (Chauhan et al., 2014; Esteve-Turrillas et al., 2007; Ras et al., 2009). The subgroup of VOCs includes benzene, toluene, ethylbenzene and xylene. These compounds are toxic in nature and play an important role as ozone precursors. Neurological disorders, headaches and short-term memory loss are some of the health disorders caused by chronic exposure to toluene and xylene (ATSDR 2005^a,b,c,d; Moolla et al., 2015; Sekar et al., 2019).

The present study aims to assess the concentration of precursors of ozone and their impact on human health in an area with prevailing residential activity in the vicinity of the power plant. Mitigation of air pollution due to ozone requires the understanding of its formation mechanism and role of each precursor in its modulation. The importance of the precursor variables in the ozone formation mechanism is therefore analysed using random forest algorithm. The comparative analysis is carried out on the data observed during activity and no-activity periods to qualitatively infer out the contribution of emission sources of precursor variables. It is of the contention that awareness in terms of public health risk is easily understood by policymakers and can be used as an instrument to sensitize common public as information on air pollutant concentration directly is not comprehensible by them. Human health assessment is therefore carried out using hazard quotient, which can suggest the severity of pollution and associated health risk in the area.

The study is carried out for two cities, namely Chandrapur and Nagpur where the power plant is one of the dominant air pollution emitting sources in addition to traffic, mining activities, small scale industries and construction activities. The two cities have different characteristics as Chandrapur has major coal mines with the dominance of coarse particulates, whereas Nagpur has recently witnessed huge infrastructural development. The construction activities have caused emission of coarse particulates whereas traffic movement has caused fine particulate matter emissions in the area. The study is also carried out on the coarse and fine particulate matter in addition to secondary air pollutants to account for the total hazard to human health caused covering the major air pollutants. 1-Hourly data on PM_2.5, PM₁₀, SO₂, NO₂, NO, O₃, benzene, ethylbenzene, toluene, xylene (BTEX) along with the meteorological data is considered during 2016–2019. The site is residential in nature in both the cities. The study area location is shown in Fig. 1. The data is obtained from Central Pollution Control Board (CPCB, 2020).

To determine the health risks associated with exposure to PM_2.5, CO, O₃, NO₂, NO, benzene, toluene, ethylbenzene and xylene by inhalation, the methodology described by US Environmental Protection Agency’s human health risk assessment framework (USEPA, 2010) is followed. Daily exposure or dose of inhalation of chemical can be determined as;

ADD=(C x IR x EF x ED)/(BW x AT) (1)

Where ADD is the average daily dose for inhalation of the chemical of interest (µg/kg/day), C is the concentration of the chemical (µg m^− 3), IR is the inhalation rate (m³ hour^− 1), ED is the exposure duration (days), BW is the body weight (kg) and AT is the averaging time (days). The exposure duration (ED) can be obtained as;

ED = ET x EF x DE (2)

Where ET is the exposure time (hour/day), EF is the exposure frequency (days/year), DE is the duration of exposure (year). The Hazard Quotient (HQ) can be given as;

HQ = ADD/RfC (3)

Where RfC is the reference dose at which significant adverse health effects occur in exposed groups. Guideline value specified by WHO or CPCB can be considered for this purpose. An HQ < 1.0 indicates a no adverse health risk, even to a sensitive individual. An HQ > 1.0 indicates some exposure risks to sensitive individuals. Adding the hazard quotient for all the pollutants, hazard index can be obtained as (Morakinyo et al., 2017);

$$HI={\sum }_{i=1}^{n}{HQ}_{i}$$

Where i is the species or pollutant of interest, n is the number of pollutants.

Random forest model is based on supervised training methods. It has been applied in the field of air pollution for regression (Kaminska, 2018; Stafoggia et al., 2020; Jiang et al., 2020). The random forest model is the generalization of the decision tree model. It builds the forests based on the ensemble of decision trees called ‘bagging method’ to get accurate and consistent results. Using the random selection of features, each decision tree is trained on bagged data. It is mainly applied to classification and regression problems. The insight into the decision process can be understood by feature importance based on the performance of the model. The feature space is divided into M regions as R_m. M is the number of leaves in the tree and c_m is the constant for the region m determined while training the decision tree, which is considered as the mean of the response variables that belong to region R_m in cases of regression problem and ratio in case of the classification problem. I is the indicator function (returning 1 if x∈R_m, 0 otherwise). The decision taken is based on the paths from the root of the tree to the leaves. The decisions are decided based on model performance.

$$f\left(x\right)=\sum _{m=1}^{M}{c}_{m}I(x,{R}_{m})$$

The average of the J trees in the forest is obtained as;

$$F\left(x\right)=\frac{1}{J}\sum _{j=1}^{J}{f}_{j}\left(x\right)$$

In the case of the regression problem, the above equation is simplified as;

$$F\left(x\right)=\frac{1}{J}\sum _{j=1}^{J}Cj+\sum _{k=1}^{K}\left(\frac{1}{J}\sum _{j=1}^{J}{Contribution}_{j}(x,k)\right)$$

The above equations are in-built in randomForest module in R package.

The status of ambient air quality in terms of PM₁₀, PM_2.5, SO₂, NO₂, NO, CO, BTEX over different years is given in Fig. 2 for Chandrapur and in Fig. 3 for Nagpur.

5.1 Chandrapur

Average PM₁₀ and PM_2.5 concentration is almost constant throughout the study years with variability in maximum concentration (refer Fig. 2). Further, PM₁₀ is higher than its threshold limit during all the years, whereas average PM_2.5 approaches the threshold concentration. Coarse particulate pollution is significant and fine particulate matter pollution is moderate as average PM_2.5/PM₁₀ ratio is observed to be < 0.5. Opencast mining activities cause coarse particulate matter emissions. Combustion due to household heating, coal-fired power plants and transportation emits fine particulate matter. As evident, the dominance of coarse particulate matter suggests the dominance of mining activity followed by a moderate contribution of fine particulate matter by the power plant and other combustion-related emissions. Unlike particulate matter, the monotonous trend is observed in most of the gaseous pollutants. CO concentration is observed to be decreasing, whereas NO₂, SO₂ and O₃ concentrations are increasing. The non-monotonous trend is observed in benzene, toluene, ethylbenzene and xylene. All the gaseous pollutants such as CO, NO₂, O₃, SO₂, benzene, toluene, ethylbenzene and xylene are well below their standard limits.

Hourly diurnal variations in all the pollutant concentrations in Fig. 3 show that average concentration does not vary significantly, however, the maximum concentration and upper quartiles significantly vary diurnally. The diurnal variation in PM₁₀, PM_2.5 and CO concentration is bi-modal with a high concentration in the evening and night hours. The concentrations peak during late-evening and night hours. A peak is again observed in the morning hours i.e. 7–10 am with troughs during day-time. During the morning hours and evening (till late evening hours), the movement of vehicular traffic is usually observed. The coincidence of maxima and minima in PM₁₀, PM_2.5 and CO with vehicular traffic movement is therefore observed. Calm atmospheric conditions led to high concentration during early night hours. Like PM₁₀, PM_2.5 and CO, the pollutants NO, benzene, toluene and xylene concentrations show similar but moderate variations. NO₂ concentration does not show any significant variations. Ethylbenzene shows higher concentration during late noon hours lasting till evening. As expected, O₃ concentration shows higher levels in day-time as compared to evening and night-time. A strong correlation of O₃ concentration with solar radiation is observed. Considerable variations are observed in SO₂ concentration minimums and maximums along with upper quartiles. Day-time high SO₂ concentration suggests the possible contribution of emissions from the power plant.

Monthly variations in all the parameters (refer Fig. 4) show that particulate matter is lower in monsoon months (mid-June till September) as compared to winter (December-February) and summer months (march-mid June). Rainfall scavenging effect is visible for PM₁₀ and PM_2.5 concentration. CO concentration shows similar variations as PM concentration. NO₂ concentration is significantly high in monsoon months, even higher than the concentration in winter and summer months. Washout effect in wet months is not evident in NO₂ concentration. Average NO concentration does not show significant monthly variation, whereas upper quartile of NO concentration shows variations with a high concentration in winter months (January February and December) and post-monsoon months (October and November). Summer months show relatively lower concentration than monsoon months. Benzene, toluene, ethylbenzene and xylene show similar variations as NO₂ and NO concentration. Ethylbenzene, however, shows the lower concentration in post-monsoon months. Like NO concentration, average O₃ concentration does not show variations but upper quartile of O₃ concentration shows significant variations with a high concentration in February-July and October.

There is a strong positive correlation between BTEX (refer Table 1), which are also observed to be correlated positively with CO, PM₁₀, PM_2.5, NO and NO₂. A moderate correlation of CO is observed with PM₁₀, PM_2.5 and strong correlation with BTEX, NO and NO₂, which suggests a similarity in their emission sources. O₃ is negatively correlated with relative humidity only. Although O₃ data with an hourly resolution is not correlated with other pollutants, the correlation was assessed for other resolutions like monthly and hourly averages. Correlation of monthly O₃ time series is however observed to be not significantly correlated with its precursors. Hourly variation time series showed the correlation of O₃ with CO, NO, NO₂ and BTEX as -0.48,-0.92,-0.11 and − 0.37 which suggests that O₃ formation mechanism is driven by both VOCs (here BTEX), CO and NOx. On a finer time scales or at high resolution, O₃ formation mechanism can be understood, whereas on the low resolution, the effect of confounding factors such as seasonality distorts the correlation of O₃ with its precursor variables. This suggests that the sources of these pollutants are similar in nature. The correlation of particulate matter with ozone precursors indicates the similarity in the sources of these pollutants.

Table 1

Correlation of Various Air Pollutant Concentrations in Chandrapur
Species	Ben	Tol	EBen	X	WD	WS	Temp	CO	PM₁₀	PM_2.5	RH	SR	O₃	NO	NO₂
Ben	1
Tol	0.09	1
Eth-B	0.90	0.88	1
X	0.69	0.20	1	1
WD	-0.01	-0.01	-0.03	-0.01	1
WS	0.02	-0.04	-0.03	0.01	0.19	1
Temp	-0.01	-0.04	0.03	-0.03	0.12	0.11	1
CO	-0.02	0.09	0.01	-0.01	-0.09	-0.14	-0.07	1
PM₁₀	0.00	0.07	0.01	0.01	-0.14	-0.16	-0.15	0.37	1
PM_2.5	-0.01	0.05	0.03	0.00	-0.11	-0.13	-0.13	0.26	0.53	1
RH	-0.03	-0.02	0.01	-0.02	0.21	-0.09	0.04	-0.09	-0.25	-0.06	1
SR												1
O₃	-0.02	-0.03	0.00	-0.02	-0.03	-0.06	0.10	-0.04	-0.10	-0.08	-0.01		1
NO	-0.01	0.07	0.02	0.01	0.00	-0.17	-0.09	0.33	0.29	0.26	0.01		-0.15	1
NO₂	0.02	0.05	-0.01	-0.02	-0.03	-0.13	0.09	0.18	0.12	0.03	0.05		0.19	-0.05	1
Ben: Benzene, Tol: Toluene, Eth-B: Ethyl Benzene, X: Xylene, WD: Wind direction, WS: Wind speed, Temp: Temperature, RH: Relative humidity, SR: Solar radiation

5.2 Nagpur

Like Chandrapur, Average PM₁₀ and PM_2.5 concentration in Nagpur is also almost constant throughout the years with variability in the upper quartiles (refer Fig. 5). PM₁₀ is higher than its threshold limit during all the years, whereas average PM_2.5 approaches the threshold concentration. PM_2.5/PM₁₀ ratio is observed to be 0.5–0.68 during four years suggesting the presence of the fine particulate matter. Traffic emissions are dominant in Nagpur followed by household combustion and power plant emissions causing the significance of fine particulate matter. The trend is not monotonous in the gaseous pollutants such as NO₂, SO₂, O₃, benzene, toluene, ethylbenzene and xylene, whereas CO concentration is decreasing like Chandrapur. All the gaseous pollutants except toluene and xylene are well below the CPCB threshold.

Like Chandrapur, hourly variations in all the pollutant concentrations show that average concentration do not vary significantly, however, the maximum concentration and upper quartile significantly vary diurnally (refer Fig. 6). A similar bi-modal diurnal behaviour of PM₁₀ and PM_2.5 concentration is observed in Nagpur like Chandrapur with a high concentration in the evening and night hours. As compared to Chandrapur, however, the bi-modality is not pronounced. The concentrations peak during late-evening and night hours. During the morning hours and evening (till late evening hours), the movement of vehicular traffic is usually observed. The maxima and minima coincided with the traffic movement extent. Benzene, toluene, ethylbenzene and xylene show similar variations with high concentration during late-evening hours till 11’o clock in the night. This can be attributed to prevailing calm weather conditions in the late evening to early night hours. The similarity in the variation pattern of CO, NO, NO₂ and BTEX compounds indicates their similarity in the source and dispersion pattern (Tiwari et al., 2010).The reactive species are generally not abundant during day time due to their involvement in photochemical activity (Tiwari et al., 2010). The diurnal variability in O₃ concentration concurs well with the solar radiation variability. Like Chandrapur, day-time high SO₂ concentration suggests the possible contribution of emissions from the power plant.

Monthly variations plot (Fig. 7) shows that particulate matter is lower in monsoon months due to the washout effect of rains. CO, NO and O₃ concentration does not show significant monthly variations and almost remain constant throughout the year. Higher NO concentration is observed in August. Summer months show relatively lower concentration than monsoon months. Lower O₃ concentration is observed in monsoon months. NO₂ concentration is initially lower in July and August but then increase in September and again get reduced in October. Benzene, toluene, ethylbenzene and xylene show similar variations as NO₂ and NO concentration.

Correlation of pollutants throws some insight into the variability in their emission sources. It can be seen from Table 2 that in Nagpur, O₃ is not correlated significantly with any of the precursor variables. PM₁₀ is correlated well with PM_2.5. Benzene is correlated significantly with toluene, xylene and ethylbenzene. With other pollutants like CO, NO and NO₂ also, a good correlation of benzene is observed. PM₁₀ and PM_2.5 are however moderately correlated with benzene. The correlation of BTEX compounds with CO, NO and NO₂ suggest their common source. CO is correlated well with NO and NO₂ and moderately with PM₁₀. CO is mainly emitted from the vehicular emissions, which is, therefore, the common source of these pollutants in the area. SO₂ is not correlated with any of the pollutants. O₃ is moderately correlated with temperature and solar radiation which suggests the photochemical reactions are governing the O₃ levels in the area.

Table 2

Correlation of Various Air Pollutant Concentrations in Nagpur
Species	B	Eth-B	X	T	WD	WS	Temp	CO	PM10	PM2.5	RH	SR	O3	NO	NO2	SO2
Ben	1
Eth-B	0.88	1
X	0.90	0.87	1
T	0.92	0.83	0.96	1
WD	0.19	0.21	0.19	0.22	1
WS	-0.04	-0.04	-0.04	-0.02	0.00	1
Temp	-0.27	-0.22	-0.22	-0.15	-0.04	0.10	1
CO	0.86	0.76	0.83	0.86	0.24	-0.01	-0.11	1
PM₁₀	0.65	0.51	0.57	0.57	0.09	-0.09	-0.20	0.53	1
PM_2.5	0.52	0.35	0.41	0.40	0.02	-0.07	-0.24	0.38	0.74	1
RH	0.07	0.09	0.08	0.06	0.10	0.04	-0.57	0.00	-0.12	-0.07	1
SR	-0.24	-0.23	-0.23	-0.21	-0.11	0.11	0.55	-0.18	-0.11	-0.14	-0.38	1
O₃	-0.14	-0.16	-0.11	-0.13	-0.18	-0.02	0.34	-0.12	0.10	0.07	-0.47	0.20	1
NO	0.71	0.73	0.75	0.69	0.22	-0.06	-0.20	0.80	0.40	0.26	0.07	-0.22	-0.14	1
NO₂	0.64	0.60	0.68	0.67	0.19	-0.09	-0.09	0.71	0.45	0.30	-0.11	-0.27	-0.12	0.64	1
SO₂	0.03	0.00	0.02	0.03	-0.19	0.00	0.22	0.06	0.12	0.09	-0.35	0.17	0.26	0.01	0.09	1
Ben: Benzene, Eth-B: Ethyl Benzene, X: Xylene, WD: Wind direction, WS: Wind speed, Temp: Temperature, RH: Relative humidity, SR: Solar radiation

5.3 BTEX ratios

The ratio of BTEX compounds has been studied in the literature to assess the nature of emissions in the area. Xylene to ethylbenzene ratio (X/Eth-B) has been used to assess the contribution of distant or local emission sources. A high ratio suggests the local emissions, whereas a low ratio indicates the contribution from distant sources (Tiwari et al., 2010). The average ratio in Chandrapur is observed to be 4 and in Nagpur, it is 4.65 suggesting the local hydrocarbon source adding to the BTEX concentration in the two cities. Toluene to benzene ratio (T/B) has been used in the literature to assess the relative contribution of BTEX sources (Breton et al., 2020). A low T/B ratio suggests contribution mainly derived from vehicles, whereas high values indicate the contribution other than the vehicles. T/B ratio is observed to be 7.73 and 4.63, for Chandrapur and Nagpur respectively. Lower T/B ratio in Nagpur suggests the contribution of vehicular emissions (Lee et al., 2002; Garg et al., 2019). Higher T/B ratio in Chandrapur suggests the closeness of point sources (Tiwari et al., 2010).

5.4 Application of random forest model

Random forest model is applied with O₃ concentration as the response variable and NO₂, NO, SO₂, CO, BTEX, PM₁₀ and PM_2.5 as independent variables. With 70:30 as the training and testing ratio and 10 fold cross-validations, the results are obtained. The model uses R², Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) as the performance measures for its evaluation. The prediction results show R² = 0.92, RMSE = 8.9 and MAPE = 11.6 for Chandrapur and R² = 0.96, RMSE = 6.7 and MAPE = 9.4 for Nagpur, which suggests that the predictions are quite reasonable. The feature importance assessment matrix which is the in-built feature of the random forest model is shown in Fig. 8. It can be seen that PM₁₀, NO and CO along with solar radiation are observed to be highly important variable governing the O₃ dynamics in Chandrapur. Meteorological variables are relatively less important than precursor variables. BTEX and PM_2.5 are the least important variables. In Nagpur, wind direction, relative humidity, temperature and toluene are important. NO₂ is more important than CO in governing the O₃ dynamics. In general, BTEX is least important in governing the O₃ variability in both the cities. The primary pollutants such as CO, NO and NO₂ are more important and modulate the O₃ dynamics. In Nagpur, meteorology plays an important role in O₃ formation, whereas in Chandrapur, only solar radiation is more important amongst meteorological variables.

Further, to attribute the levels of pollutants to emission sources, a qualitative analysis is carried out for the two time periods as; with and without emission activities. In India, due to COVID19, the complete lockdown was initiated during March 22, 2020 to April 20, 2020. During this period, only essential services were retained with the restriction on vehicular movement. Heavy-duty vehicles were allowed only to carry the essential services loads that too were plying outside the city centres. Traffic-related activities, construction, solid waste burning were completely banned. The only allowed activities related to the air pollution emissions were power plant and household emissions from combustion. The contention of analyzing the data during the lockdown and during the normal period is that if the banned activities are contributing to the air pollution in the area, their shutting down will considerably reduce the air pollution concentrations during the lockdown period. No significant reduction in the air pollution levels may also suggest the contribution from running activities such as power plants and domestic heating. A comparison analysis of pollutant concentration is therefore carried for the lockdown period during 2020 and the corresponding pollutant concentration during 2019 for the same period i.e. March 24, 2019 to April 20, 2019.

It can be seen from Table 3 that benzene, toluene, xylene has reduced by 70–88% in Chandrapur and by 77–98% in Nagpur during the lockdown period. This suggests that traffic-related emissions significantly contributed to the BTEX in these two cities. In Chandrapur, PM₁₀ and PM_2.5 reduced by about 37% and 18% and in Nagpur, both reduced by about 51% and 54%, respectively. Although the reduction in PM is significant at 5% level of significance in both the cities, it is not pronounced like BTEX compounds. This indicates that apart from the traffic emissions, construction, power plant emissions and domestic combustion also add to the PM levels in the cities. CO is reduced by 30% in Chandrapur and by 60% in Nagpur. NO and NO₂ has reduced considerably in Nagpur, but in Chandrapur, NO₂ rather has increased and reduction in NO is not significant. O₃ is observed to be increased in the lockdown period in Chandrapur, whereas in Nagpur the significant reduction of about 50% is observed. This suggests the influence of traffic emissions in Nagpur and dominance of non-traffic related emissions, mainly power plant and mining activities in Chandrapur.

Table 3

Pollutant Concentrations during COVID-19 Lockdown Period and the Corresponding Period in 2019
Species	Chandrapur				Nagpur
Species	2020	2019	%reduction	significance	2020	2019	%reduction	significance
Benzene	0.1	0.34	70.6	*	1.32	5.76	77.1	*
Toluene	0.2	1.72	88.4	*	0.35	22.61	98.5	*
Eth-Benzene	-	-	-		4.12	4.89	15.7	ns
Xylene	0.2	1.29	84.5	*	1.67	16.46	89.9	*
PM₁₀	81.8	129.63	36.9	*	47.47	96.60	50.9	*
PM_2.5	38.2	46.72	18.2	*	23.18	50.01	53.6	*
CO	0.3	0.43	30.2	*	0.46	1.15	60.0	*
NH₃	75.8	25.20	-	*	32.23	41.11	21.6	*
NO	3.4	3.83	11.2		3.46	13.81	74.9	*
NO₂	13.3	12.47	-		21.85	43.97	50.3	*
SO₂	25.1	8.52	-	*	-	18.72	-
O₃	22.2	20.59		*	58.14	116.03	49.9	*
* significant at 0.05 level of significance, ns-not significant

5.5 Heath risk assessment

Hazard quotient is obtained for different pollutants based on the assumption of chronic exposure of the population. Assuming the exposure time (ET) of 24 hours/day, duration of exposure (DE) as 30 years and exposure frequency (EF) of 312 days/year, exposure duration (ED) is computed. The averaging time (AT) is estimated by multiplying the duration of exposure and 365 days/year. Average inhalation rate (IR) for an adult is 20 m³/day (Van den Berg, 1995). Bodyweight of average Indian male is 70 kg and the average Indian female is 60 kg. Average body weight of 65 kg is therefore considered. Reference concentration for different pollutants is considered based on their respective standard provided by CPCB (2009). The reference concentration for BTEX compounds, NO₂, SO₂, CO, O₃, PM₁₀ and PM_2.5 is considered as 5, 40, 50, 2, 100, 60 and 40 µg m^− 3, respectively. It can be seen from Table 4 that hazard quotient for PM_2.5 and PM₁₀ is relatively higher than other pollutants and ranges between 0.33–0.64 for Chandrapur. All other pollutants have hazard quotient < 0.17 for Chandrapur. Higher hazard quotient is observed for toluene in Nagpur ranging from 0.40–1.35 during 2016–2019. For PM_2.5 and PM₁₀, it ranges between 0.29–0.59. Comparing the hazard quotient of Nagpur with Chandrapur, it is higher for ethylbenzene, xylene, benzene and NO₂ in Nagpur than in Chandrapur. In a study on the morbidity and mortality due to respiratory and cardiovascular diseases, the excess number of mortality and morbidity in both the cities is observed to be increasing (Maji et al., 2016). Overall, the hazard index is 1.22–1.67 for Chandrapur and 3.07–4.35 for Nagpur. Hazard index > 1 in both cities suggests the health hazard to the residents living in the area.

Table 4

Hazard Quotient of Various Pollutants at Chandrapur and Nagpur
Pollutant	Chandrapur				Nagpur
Pollutant	2016	2017	2018	2019	2016	2017	2018	2019
CO	0.09	0.09	0.07	0.05	0.07	0.05	0.15	0.13
O₃	0.03	0.03	0.05	0.05	0.15	0.09	0.11	0.15
NO	0.06	0.04	0.02	0.04	0.08	0.10	0.11	0.16
NO₂	0.07	0.10	0.13	0.10	0.36	0.18	0.25	0.34
SO₂	0.04	0.05	0.05	0.06	0.06	0.07	0.07	0.06
PM_2.5	0.48	0.41	0.33	0.30	0.29	0.49	0.32	0.31
PM₁₀	0.61	0.64	0.54	0.49	0.38	0.52	0.42	0.38
Benzene	0.01	0.01	0.05	0.01	0.14	0.12	0.36	0.31
Toluene	0.04	0.10	0.04	0.06	0.64	0.40	1.35	1.03
Eth-Benzene	0.06	0.10	-	-	0.23	0.34	0.30	0.34
Xylene	0.17	0.08	0.14	0.05	0.67	1.06	0.91	0.81
Hazard Index (HI)	1.65	1.67	1.42	1.22	3.07	3.43	4.35	4.01

Study of air pollutant concentrations such as PM₁₀, PM_2.5, CO, NO, NO₂, SO₂, BTEX and O₃ has been carried out at a residential site in the vicinity of the coal-fired power plant in two cities during 2016–2019. In Nagpur, the trend is observed to be insignificant in all the pollutant concentrations except in CO, whereas in Chandrapur, the trend is observed to be insignificant only in BTEX, PM₁₀ and PM_2.5 concentration and increasing in NO₂, SO₂ and O₃. The diurnal variation showed the coincidence of maximum in PM₁₀, PM_2.5 and CO with high vehicular traffic movement time. Prevailing calm atmospheric conditions led to high concentration of BTEX and other gaseous pollutant during early night hours. Strong positive correlation of BTEX among themselves and with CO, PM₁₀, PM_2.5, NO and NO₂ suggested a similarity in their emission sources. O₃ is observed to be not correlated significantly with any of its precursor variables. On different time scales therefore the correlation of O₃ with precursor variables is computed. On a finer time scale, a significant correlation of O₃ with CO, NO, NO₂ and BTEX suggested that O₃ formation mechanism is driven by VOCs (mainly BTEX), CO and NOx. On the coarser scale however seasonality and other factors have distorted the correlations which are not evident in the time series. Ratio analysis to understand the emission source contribution of BTEX compounds suggested the presence of the local hydrocarbon source. Toluene to benzene ratio suggests the contribution of vehicular emissions in Nagpur and the contribution of point sources in Chandrapur. Random forest model with O₃ concentration as the response variable and NO₂, NO, SO₂, CO, BTEX, PM₁₀ and PM_2.5 as independent variables suggested that PM₁₀, NO, CO and solar radiation are highly important variable governing the O₃ dynamics in Chandrapur. Meteorological variables are relatively less important than precursor variables. In Nagpur, wind direction, relative humidity, temperature, toluene and NO₂ are more important. In general, BTEX is least important in governing the O₃ variability in both the cities. Further, qualitative analysis of the assessment of the contribution of emission sources, suggested that BTEX are contributed by traffic-related emissions. PM₁₀ and PM_2.5 are contributed by construction, power plant emissions and domestic combustion, apart from the traffic emissions. Overall, the influence of traffic emissions in Nagpur and dominance of non-traffic related emissions, mainly power plant and mining activities in Chandrapur is observed. Hazard quotient for PM_2.5 and PM₁₀ is observed to be relatively higher than other pollutants. Hazard index is observed to be > 1 in both the cities suggesting the health hazard to the residents living in the area.

-Ethical Approval :Not applicable.

-Consent to Participate: Not applicable.

-Consent to Publish (Missing) :Not applicable.

-Funding: No Funding

-Competing Interests: The authors declare that they have no competing interests.

-Availability of data and materials: Not applicable.

Acknowledgements The authors are thankful to the Karunya Institute of Technology and Sciences, for their guidance and unstinted support for this study.

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Study of ground ozone and precursors along with particulate matter at residential sites in the vicinity of a power plant

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Abstract

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1. Introduction

2. Study Area And Data Used

3. Human Health Risk Assessment

4. Random Forest Model

5. Results And Discussion

5.1 Chandrapur

5.2 Nagpur

5.3 BTEX ratios

5.4 Application of random forest model

5.5 Heath risk assessment

6. Conclusion

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