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 NO_x analyser, Serius 500 Portable Air Quality Monitor (with swappable sensor heads) to get O₃, NO_x (NO+NO₂), total VOC (TVOC), CO, and SO₂ concentrations, respectively. The measurement was carried out on National Highway 49 (NH-49) (22.379128°N, 87.361647°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 (τ) with a strong performance at the median quantile (τ = 0.5) that explained 76.06% of variability in O₃ concentration (R¹(τ)_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(τ) were diminished at lower (below τ = 0.1) and higher quantiles (above τ = 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 ‘Moderately Polluted’ region. Overall, NO_x 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 NO_x emissions are essential.