Impact of CRB on Production of Surface Ozone in Punjab Plains of North-West Indo-Gangetic Plains: Development of Prediction Model using Statistical Approach

Abstract

Air quality is a complex function of emissions, meteorology and topography and a sound framework for the connection of these variables can be provided by statistical approaches. Among the most significant contributors of air pollution like automobiles, industries and domestic emissions, the post-harvest crop residue burning is an episodic contributor to the pollutants level in the IGP region. Rice and wheat are the major crop in the North-Western part of the country, which generate a large amount of leftover crop residue, intensified with the usage of mechanical combine harvester technology which is further, subjected to open field crop residue burning. Natural and anthropogenic activities emit aerosols and trace gases such as carbon monoxide (CO) and oxide of nitrogen (NOx), methane (CH4), total nonmethane (TNMHCs), which impart significant role in the lower atmosphere’s reactivity. Meteorology, photochemistry, emissions, and deposition are the major factors that lead to production and accumulation of secondary pollutants like peroxy-acetyl nitrate (PAN) and surface ozone. This research provides a continuous in situ measurements of gaseous pollutants (surface O3, CO, NOx, MHC, TNMHCs), and meteorological variables (AT, SR, RH, WS and WD) for a time span of four years between 1st January 2013 to 31st December 2016, at a semi-urban site (Patiala) of North-West Indo-Gangetic Plains (NW-IGP). The meteorological impact on gaseous pollutant has been studied on a daily, monthly, seasonal and diurnal scale. The daily concentrations of gaseous pollutants like O3, CO, NOx, MHC, and TNMHCs ranged from 5 to 83 ppb, 0.01 to 2.6 ppm, 2.3 to 113.5 ppb, 562 to 3166 ppb and 27 to 413 ppb, respectively. The meteorological variables RH, SR, AT and WS range between 33.5 to 95.0 %, 68.0 to 378 W m-2, 8 to 38 °C and 0.05 to 7.85 m sec-1, respectively. In diurnal ozone profile, a steep minimum is observed during the early morning around 0600- 0800 hours and rise in ozone concentration is observed after 0800 hour, and hit maxima around in the range of 1500-1600 hours and starts decreasing after late afternoon or evening, in each season with variations in amplitude. The precursor gases exhibited two maxima, during the morning (0700-1000 hours) and evening hours (1700-1900 hours), correspond to boundary layer and prominent anthropogenic emissions during evening hours, when the vehicular activity is maximum and lower mixing ratios in the afternoon hours (1200-1600 hours) influenced by rise in the boundary, provides favorable dispersion conditions coupled with reduced vehicular emissions. Comparison of diurnal ozone profiles with those of precursor gases (CO, NOx, MHC, and TNMHCs) showed that maximum O3 concentration was coincident with the concentration minima of NOx and CO. This reveals the photochemical production of ozone resulted from the reaction of CO, CH4, and NMHCs with OH radicals in the presence of NOx. On seasonal basis, precursor gas (NOx, CO, MHC, TNMHC), delineated the higher levels in winter and post monsoon season followed by summer and monsoon season. Ozone is negatively correlated with its precursor gases CO ( 0.250), NOx (-0.262), MHC (- 0.151) and TNMHCs (-0.252), as they get consumed in the photochemical production of ozone. The precursor gases (CO, NOx, MHC, and TNMHCs) showed a strong positive correlation with each other which is attributable to common anthropogenic emission sources such as road traffic and combustion sources. The impact of meteorology on ozone production was positively correlated with SR (0.565), AT (0.449), and WS (0.208) and negatively correlated with RH (-0.682), which highlights the importance of photochemistry in the formation of ozone. CO, NOx, MHC and TNMHCs were negatively correlated with SR (- 0.557, -0.477, -0.300, and -0.441), AT (-0.527, -0.485, -0.320, -0.355), and WS (- 0.401, -0.392, -0.268,-0.468) and positively correlated with RH (0.222, 0.130, 0.217, 0.123) respectively. The correlations observed in all cases are statisticaly significant (p<0.01). Hence, primary pollutants concentration gets declined as the photochemical reaction for theproduction of secondary pollutant (O3) become more active with increasing solar radiation, ambient air temperature, and wind speed. The contribution from regional pollution to long-distance aged pollutants was identified using seasonal backward trajectories and cluster analysis. The main contribution in the four clusters was from cluster 1 (56%), followed by cluster 3 (17%). The biggest influx of gaseous pollutants reached the receptor site from regional continental areas and was not substantially affected by long-range transport. The highest ozone concentrations were observed in the summer, followed by post-monsoon, monsoon and winter season. The rate of increase of ozone (dO3/dt) is highest during post-monsoon area varies from 14.3 to 18.4 ppb/hour, followed by the summer season (10.2 to 13.5 ppb/hour), indicating the influence of increased emission of precursor gases, especially from the episodic crop residue burning event in respective periods. Ozone exceedance of 24 hour national standard occurred on 50% and 8% of the sampling periods during wheat and rice crop residue burning, respectively. The daily O3 max / O3 min ratio is a pollution index, value of the order 15.60 shows that the location of the study has significant ozone pollution. A wide range of statistical methodologies were applied to develop satisfactory predictions of daily ozone levels. The development of statistical models involved the detailed choice of both site-specific predictor variables (pollutant and meteorological variables) along with the interactions between predictor variables in order to better capture the target variable (ozone) behavior. Random forest and backward stepwise regression methods are used for the importance and selection of the variables using R packages. It is found that among meteorological variables relative humidity and in precursor gases NOx has the highest impact on the production of ozone. The fourteen prediction algorithms and their possible combinations of ensemble models were employed in this study. Compared with individual models the ensemble model approach showed an index of agreement of 0.91, the accuracy of 95.5% and mean absolute error of -0.001 ppb between the predicted and observed diurnal cycle and daily averaged data of the year 2016 for benchmark analysis. Predictions on air quality are very coherent and efficient in controlling the measures and can be proposed as a preventive action for regulations and protect public health.