Gaseous Pollutants Driven Ozone Variability

Ground-level ozone (O3) is a secondary air pollutant and one of the major air pollutants that shape the atmospheric chemistry and influence many chemical reactions in the atmosphere. O3 is the second most significant air pollutant after particulate matter contributing mortality world-wide. To explore the O3 (a criteria pollutant) concentration variability influenced by primary air pollutants, especially criteria air pollutants such as volatile organic compounds (VOC), carbon monoxide (CO), nitrogen dioxide (NO2), and sulfur dioxide (SO2), we conducted a field campaign at Kharagpur city in India on April 2024. Sample air was measured by the USEPA approved Serinus 10 ozone analyser, Serinus 40 NOx analyser, Serius 500 Portable Air Quality Monitor (with swappable sensor heads) to get O3, NOx (NO+NO2), total VOC (TVOC), CO, and SO2 concentrations, respectively. The measurement was carried out on National Highway 49 (NH-49) (22.379128&#176;N, 87.361647&#176;E) in Kharagpur. Results showed a strong negative correlation between O3 and NO (r = -0.82), a weak positive correlation with NO2 (r = 0.19), moderate negative correlations with TVOC (r = -0.54) and CO (r = -0.49), and a very weak positive correlation with SO2 (r = 0.11). All correlations are statistically significant at the p < 0.01 level. We applied Quantile Regression Model (QRM) to explore a robust framework for analyzing the relationships between dependent (O3) and independent variables (NO, NO2, TVOC, CO, SO2) across different points of the data distribution by capturing conditional quantiles. Analysis revealed nonlinear distribution of O3 concentration across all the quantiles (&#964;) with a strong performance at the median quantile (&#964; = 0.5) that explained 76.06% of variability in O3 concentration (R1(&#964;)0.5) = 0.7606) with high accuracy and low predicting errors (MAE = 14.19, RMSE = 17.86). The local measure of goodness of fit, R1(&#964;) were diminished at lower (below &#964; = 0.1) and higher quantiles (above &#964; = 0.95) and the Quantile Loss of 7.10 confirms effective handling of O3 variability. The standardized coefficients for NO were negative across all quantiles and became less negative at higher quantiles (0.8-1.0) that indicated a weaker adverse effect as O3 increased. NO2 showed positive coefficients that peaked at the 0.4 quantile and declined at higher quantiles and suggesting a stronger influence at moderate O3 levels. TVOC consistently exhibited negative coefficients with a stronger effect at lower quantiles (0.1-0.2) that stabilizes at higher quantiles. CO and SO2 coefficients fluctuate around zero and shows minimal and inconsistent influence. Monte Carlo simulation of health risk assessment showed that O3 could significantly pose the development of non-carcinogenic health risks (Hazard Quotient (HQ) > 1). Sensitivity analysis revealed the variance in the O3 Hazard Index (HI) where NO significantly contributed the most (60.4%), followed by NO2 (23.3%), TVOC (12.1%), SO2 (4.0%), and CO (0.2%). The Air Quality Index (AQI) analysis categorized Kharagpur as a &#8216;Moderately Polluted&#8217; region. Overall, NOx and TVOC are the two types of major gaseous pollutants that contributed majorly in O3 concentration variability, thus O3 pollution levels. Targeted policies to reduce VOC and NOx emissions are essential.