Trends of Ground-Level Ozone in New York City Area during 2007–2017

The spatiotemporal patterns of ground level ozone (O3) concentrations in the New York City (NYC) metropolitan region for the 2007–2017 period were examined conjointly with local emissions of O3 precursors and the frequency of wildfires. Daily 8-h and 1-h O3 and nitric oxide (NO) concentrations were retrieved from the US Environmental Protection Agency (EPA) Air Data. Annual emission inventories for 2008 and 2017 were acquired from EPA National Emissions Inventory (NEI). The number and area burnt by natural and human-ignited wildfires were acquired from the National Interagency Fire Center (NIFC). The highest daily 8-h max O3 concentrations varied from 90 to 111 parts per billion volume (ppbv) with the highest concentrations measured perimetrically to NYC urban agglomeration. The monthly 8-h max O3 levels have been declining for most of the peri-urban sites but increasing (from +0.18 to +1.39 ppbv/year) for sites within the urban agglomeration. Slightly higher O3 concentrations were measured during weekend than those measured during the weekdays in urban sites probably due to reduced O3 titration by NO. Significant reductions of locally emitted anthropogenic nitrogen oxides (NOx) and volatile organic compounds (VOCs) may have triggered the transition from VOC-limited to NOX-limited conditions, with downwind VOCs sources being critically important. Strong correlations between the monthly 8-h max O3 concentrations and wildfires in Eastern US were computed. More and destructive wildfires in the region were ignited by lightning for years with moderate and strong La Niña conditions. These findings indicate that climate change may counterbalance current and future gains on O3 precursor’s reductions by amending the VOCs-to-NOx balance.

Surveillance on Emission of Herbal Woods and Cow Dung for Refinement of Atmosphere with Vedic Mantra

Earth's atmosphere is made of two gases Nitrogen and Oxygen. Five major air pollutants are Ground level Ozone, Airborne particles or aerosols, Carbon monoxide, Sulfur dioxide, Nitrogen dioxide. Air pollutants risky to human health are Ground level Ozone and Aerosols. They are the main ingredients of Smog . The ground level ozone is formed when sunlight reacts with certain chemical emissions like nitrogen dioxide, carbon monoxide or methane These chemicals are emitted from industrial waste, car exhaust, gasoline vapors etc. Air quality is measured with the Air Quality Index. An AQI under 50 is considered as good air quality however as the AQI number increases , it becomes a concern for human health . Researcher measured the PM level (PM 2.5 and PM 10), temperature, Humidity and other related parameters continuously on different woods in different times in a fixed size room and constrained environment to establish that Yagya is a reliable source to reduce environment pollution .

A Comparison of Machine Learning Methods to Forecast Tropospheric Ozone Levels in Delhi

Ground-level ozone is a pollutant that is harmful to urban populations, particularly in developing countries where it is present in significant quantities. It greatly increases the risk of heart and lung diseases and harms agricultural crops. This study hypothesized that, as a secondary pollutant, ground-level ozone is amenable to 24 h forecasting based on measurements of weather conditions and primary pollutants such as nitrogen oxides and volatile organic compounds. We developed software to analyze hourly records of 12 air pollutants and 5 weather variables over the course of one year in Delhi, India. To determine the best predictive model, eight machine learning algorithms were tuned, trained, tested, and compared using cross-validation with hourly data for a full year. The algorithms, ranked by R2 values, were XGBoost (0.61), Random Forest (0.61), K-Nearest Neighbor Regression (0.55), Support Vector Regression (0.48), Decision Trees (0.43), AdaBoost (0.39), and linear regression (0.39). When trained by separate seasons across five years, the predictive capabilities of all models increased, with a maximum R2 of 0.75 during winter. Bidirectional Long Short-Term Memory was the least accurate model for annual training, but had some of the best predictions for seasonal training. Out of five air quality index categories, the XGBoost model was able to predict the correct category 24 h in advance 90% of the time when trained with full-year data. Separated by season, winter is considerably more predictable (97.3%), followed by post-monsoon (92.8%), monsoon (90.3%), and summer (88.9%). These results show the importance of training machine learning methods with season-specific data sets and comparing a large number of methods for specific applications.

Comparison of Volatile Organic Compound Concentrations in Ambient Air among Different Source Areas around Khon Kaen, Thailand

Volatile organic compounds (VOCs) are a complex group of chemicals that pose a direct risk to human health. They also lead to the formation of other air pollution constituents, including fine particulate matter (PM2.5) and ground level ozone (O₃). The ambient air concentrations of 19 VOCs were measured using multi-day 24 h sampling at two urban sites and two rural sites in the area of Khon Kaen, Thailand. Results showed that most VOCs were at concentrations considered acceptable according to the 24 h average standards established by the Thai Pollution Control Department. The VOC acrolein, however, was detected at concentrations (0.69–1.15 μg/m3) in excess of the 24 h average standard (0.55 μg/m3). Two other VOCs, benzene and 1,3-butadiene, were also detected at elevated levels (1.73–2.75 and 0.18–0.40 μg/m3, respectively) that indicated the potential to exceed the 1-year average standard. VOC concentrations were highest in the urban market monitoring site, suggesting that vehicle exhaust and food preparation using cooking oil at high temperatures may have been potential sources of the elevated VOCs.

Machine Learning Techniques Applied to Predict Tropospheric Ozone in a Semi-Arid Climate Region

In the last decade, ground-level ozone exposure has led to a significant increase in environmental and health risks. Thus, it is essential to measure and monitor atmospheric ozone concentration levels. Specifically, recent improvements in machine learning (ML) processes, based on statistical modeling, have provided a better approach to solving these risks. In this study, we compare Naive Bayes, K-Nearest Neighbors, Decision Tree, Stochastic Gradient Descent, and Extreme Gradient Boosting (XGBoost) algorithms and their ensemble technique to classify ground-level ozone concentration in the El Paso-Juarez area. As El Paso-Juarez is a non-attainment city, the concentrations of several air pollutants and meteorological parameters were analyzed. We found that the ensemble (soft voting classifier) of algorithms used in this paper provide high classification accuracy (94.55%) for the ozone dataset. Furthermore, variables that are highly responsible for the high ozone concentration such as Nitrogen Oxide (NOx), Wind Speed and Gust, and Solar radiation have been discovered.