ABSTRACT
Based on the air quality data and conventional meteorological data of the Nanjing Region from January 2015 to December 2016, to analyze the characteristics of O3 concentration changes in the Nanjing Region, a light gradient boosting machine (LightGBM) model was established to predict O3 concentration. The model was compared with three machine learning methods that are commonly used in air quality prediction, including support vector machine, recurrent neural network, and random forest methods, to verify its effectiveness and feasibility. Finally, the performance of the prediction model was analyzed under different meteorological conditions. The results showed that the variation in O3 concentration in Nanjing had significant seasonal differences and was affected by a combination of its pre-concentration, meteorological factors, and other air pollutant concentrations. The LightGBM model predicted the ground-level O3 concentration in the Nanjing area more precisely to a large extent (R2=0.92), and the model outperformed other models in prediction accuracy and computational efficiency. In particular, the model showed a significantly higher prediction accuracy and stability than that of other models under a high-temperature condition that was more likely prone to ozone pollution. The LightGBM model was characterized by its high prediction accuracy, good stability, satisfactory generalization ability, and short operation time, which broaden its application prospect in O3 concentration prediction.
ABSTRACT
AMA GC5000BTX was used to monitor the mixing ratio of benzene, toluene, ethylbenzene, m,p-xylene, o-xylene, and styrene (BTESX) in the atmosphere of the northern suburb of Nanjing from January 2014 to December 2016. The temporal variation characteristics of BTESX and the influence of meteorological elements on it were analyzed, and the characteristic ratio method (T/B) was used to qualitatively analyze the source of BTESX. Finally, the human exposure analysis and evaluation method of EPA was used to evaluate the health risk of BTESX. The results showed that during the observation period, the average mixing ratio of BTESX was (7.28±6.63)×10-9, and the mixing ratio of benzene was the highest at (2.45±3.91)×10-9. The mixing ratio of other species from large to small was toluene>ethylbenzene>m,p-xylene>o-xylene>styrene, which were (2.41±2.61)×10-9, (1.37±1.28)×10-9, (0.51±0.48)×10-9, (0.3±0.36)×10-9, and (0.22±0.42)×10-9, respectively. Due to the existence of stable aromatic sources, the monthly and seasonal variation in BTESX mixing ratio was not as obvious as that of other species (NOx, CO, SO2, PM2.5, etc.). The weekend effect of BTESX and other pollutants was not significant. The mixing ratio of BTESX was largely affected by the short distance transportation of chemical enterprises and traffic trunk roads in the northeast, resulting in a large mixing ratio of BTESX in the northeast. The mixing ratio of BTESX was jointly affected by relative humidity and temperature, and its high value area was mainly located in the range of 30%-70% relative humidity. In this range of relative humidity, the high value range of BTESX volume fraction increased with the elevation of temperature. The HI (hazard index) of BTESX in different seasons was within the safety range recognized by EPA, whereas the R (carcinogenic risk of benzene) value was higher than the safety threshold specified by EPA. At the same time, the HI and R values were higher in summer, to which great attention should be paid.
