Hourly Ozone Concentrations Research Articles

An Ozone Episode in the Urban Agglomerations along the Yangtze River in Jiangsu Province: Pollution Characteristics and Source Apportionment

A severe ozone episode occurred in cities along the Yangtze River of Jiangsu Province (UAYRJS) from 6 to 8 September 2022, with daily maximum 8-h average ozone concentrations in the range of 65.8–119 ppb, peaking in Nanjing on 7 September. We used the air quality model WRF-CMAQ-ISAM and the Lagrange trajectory model HYSPLIT to quantify the ozone contribution of each region and analyze the causes and regional transmission pathways of ozone pollution in the UAYRJS. Based on simulated emissions, we also estimated the contribution of biogenic volatile organic compounds. We found that weather has a negative impact on pollution, and ozone pollution tracks the movement of the Western Pacific Subtropical High. UAYRJS was affected by oceanic pollution, and there was a mutual influence among the area’s cities. On 6 September, the ozone in UAYRJS was mostly locally generated (50–98%); on 7 September, it was dominated by extra-regional transport (50–80%). Isoprene concentrations in UAYRJS increased by 0.03–0.1 ppb on 6 and 7 September compared with 5 September. Sensitivity testing showed that the hourly ozone concentration increased by 0.1–27.8 ppb (7.6–19.1%) under the influence of biogenic emissions. The results provide a scientific basis for future ozone control measures.

Forecasting Hourly Ozone Concentration Using Functional Time Series Model—A Case Study in the Coastal Area of Bangladesh

Forecasting Hourly Ozone Concentration Using Functional Time Series Model—A Case Study in the Coastal Area of Bangladesh

Hybrid machine learning model for hourly ozone concentrations prediction and exposure risk assessment

With the development and improvement of optimization algorithms, machine learning models have become more important in air quality prediction. These models can be used to address problems associated with atmospheric environmental pollutants like ozone and facilitate the development of appropriate control policies. Firstly, the data sets of meteorological and pollution in Nanjing and four nearby cities from 2018 to 2021 were divided into four sections to build mixed models. The extreme gradient boosting (XGBoost) algorithm was improved by the permutation importance method (PIM) and Pearson correlation coefficient to acquire the most important features of the seasonal model and analysis of the effect on ozone prediction. The results reflect that the NO2, PM2.5, and PM10 parameters have an important influence, accounting for an average of 23% of prediction, and the average influence of the meteorological parameters of the solar radiation exceeds 11%. Secondly, the particle swarm optimization (PSO), grey wolf optimizer (GWO), and ant lion optimizer (ALO) algorithms were used to optimize the SVR model parameters to predict hourly ozone concentration in January, April, July, and September 2022. The R2 values are 0.95, 0.92, 0.85, and 0.84, respectively, and the RMSE values are 5.2, 12.4, 18.0 and 16.6 μg/m3, respectively. Finally, the impact of hourly ozone concentration on population health was studied by a risk assessment of the population density-weighted pollution exposure. The result shows that the influence on humans gradually decreased yearly from 2018 to 2021, the average variation from 2.6% to −1.1%, and health risks during the evening rush hours are still prevalent and require further attention.

Estimation of near-surface ozone concentration and analysis of main weather situation in China based on machine learning model and Himawari-8 TOAR data

Ozone (O3) is an important greenhouse gas in the atmosphere. Stratospheric ozone protects human beings, but high near-surface ozone concentrations threaten environment and human health. Owing to the uneven distribution of ground-monitoring stations and the low time resolution of polar orbiting satellites, it is difficult to accurately evaluate the refinement and synergistic pollution of near-surface ozone in China. Besides, atmospheric circulation patterns also affect ozone concentrations greatly. In this study, a new generation of geostationary satellite is used to estimate the hourly near-surface ozone concentration with a spatial resolution of 0.05°. First, the Pearson correlation coefficient and maximum information coefficient were used to study the correlation between the top of atmospheric radiation (TOAR) of Himawari-8 satellite and O3 concentration; seven TOAR channels were selected. Second, based on an interpretable deep learning model, the hourly ozone concentration in China from September 2015 to August 2021 was obtained using the TOAR-O3 model. Finally, the self-organizing map method was used to determine six major summer weather circulation patterns in China. The results showed that (1) the near-surface O3 concentration can be accurately estimated; the R2 (RMSE: μg/m3) values of the daily, monthly, and annual tenfold cross validation results were 0.91 (12.74), 0.97 (5.64), and 0.98 (1.75), respectively. The feature importance of the model showed that the temperature, TOAR, and boundary layer height contributed 38 %, 22 %, and 13 %, respectively. (2) The O3 concentration showed obvious spatiotemporal difference and gradually increased from 10:00 to 15:00 (Beijing time) every day. In most areas of China, O3 concentration had increased significantly. (3) The O3 concentration in northern China was the highest under the circulation pattern of the Meiyu front over the Yangtze River Delta, while in southern China, it was the highest under the circulation pattern of the northeast cold vortex controlling most of China.

Estimation of health impact using AirQ+ model attributed to surface ozone in sub-regions of Surat city, India

Introduction: Deteriorated air quality in nation like India contributes to the health burden. The AirQ+ is used to estimate short-term and long-term health impact attributable to surface Ozone (O3) in Surat city. Average hourly ozone concentration data and other criteria pollutants retrieved from January 2018 to December 2019 from two monitoring stations (Limbayat and Varachha).
 Materials and methods: In this study, the Respiratory Mortality (RM), Cardiovascular Mortality (CM), Total Mortality (TM), Hospital Admissions with Cardiovascular Disease (HACVD), and Hospital Admissions with Respiratory Disease (HARD), as well as Respiratory Mortality-Long-Term (LT-RM) were quantified. Baseline Incidence (BI) data were obtained from literature and Relative Risk (RR) values were referred from World Health Organization (WHO). An annual Sum of Maximum 8 h Ozone means over 35 ppb (SOMO35), 70 µg/m3, used as a predictor of potential long-term health effects.
 Results: More ozone concentration were observed in winter and pre-monsoon than concentration formed in southwest monsoon and post-monsoon seasons.The average of O3 concentration for Limbayat are 71.61 (±0.39) µg/m3 and 29.76 (±1.86) µg/m3 and for Varachha are 61.17 9 (±6.15) µg/m3, 11.32 (±1.35) µg/m3 during 2018 and 2019, respectively and the obtained cumulative number of cases of death are estimated 136, 45, 172 and 18 persons respectively. Total hospital admission due to cardiovascular and respiratory diseases are found 435, 134, 552 and 58 at Limbayat and Varachha during 2018 and 2019. LT- RM is attributed to ozone concentration having the most significant value, 6.8% and 4.62% at Limbayat and Varachha in 2018.
 Conclusion: More hospital admissions are found than mortality rates using AirQ+ tool. It can be used to estimate public health in context of mortality and morbidity rates which helps to develop air quality management programs and policy makers to reduce the impact of air pollution on health.

CNN-based model for the spatial imputation (CMSI version 1.0) of in-situ ozone and PM2.5 measurements

This research presents a unique application of the convolutional neural network (CNN) for imputing data of hourly ozone concentrations between surface air quality stations. The study's objective is to develop a model that can prepare a 2D gridded map from the available observation from ground measurement. We train the CNN-based Model for Spatial Imputation (CMSI) with the Community Multiscale Air Quality (CMAQ) model-simulated hourly ozone concentrations. The modeled ozone with the CMAQ at a 27 km horizontal resolution acted as a proxy for the spatially complete and gridded ozone dataset. We also evaluated the CMSI models spatially for in-situ measurement using a 10-fold evaluation, and we found a yearly index of agreement of 0.91, a mean bias of 3.06 ppb, and a root mean square error of 10.89 ppb (an average of combined 10 sets of evaluations). In addition, we also trained two different CMSI models as case studies; i) for PM2.5 concentrations to assess its robustness for other atmospheric constituents, and ii) for regridded fine resolution (0.1⁰ × 0.1⁰) ozone concentrations. The performance of the CMSI model for the imputation of in-situ measurements was independent of the proxy model used for the specific resolution, however, using a proxy model with better physics and chemistry may improve the performance of the CMSI model. The system demonstrates the capability to accurately impute surface ozone concentrations within grids for which observation data are not available. The spatially gridded dataset generated by the CMSI model can further be used in conjunction with a numerical model for estimation and forecasting. The CMSI model can also be used for creating fine resolution gridded datasets based on in-situ measurement.

Effects of ground-level ozone pollution on yield and economic losses of winter wheat in Henan, China

Ground-level ozone (O3) is a secondary air pollutant and has negative effects on crops, especially in China in recent years due to the sharply increasing precursors of O3. Based on the hourly O3 concentrations simulated by Weather Research and Forecasting model coupled with Chemistry (WRF/Chem) and AOT40 index (accumulation of hourly ozone concentrations exceed 0.04 ppm), we assessed the losses of yield and economy of winter wheat during 2015–2018 at the county level for a central province (Henan) of China. The O3 concentration and AOT40 during the wheat growing seasons (75-days, 44 days before and 30 days after mid-anthesis) showed a clearly increasing trend over nearly all counties. The annual mean AOT40 was 6.25 ppm h, 4.32 ppm h, 5.26 ppm h, and 6.87 ppm h from 2015 to 2018, respectively. The AOT40 and the loss of relative yield of winter wheat showed significant spatial and temporal variations at the county level. The annual mean relative yield loss of wheat for Henan during 2015–2018 was 12.8%, 8.8%, 10.8%, and 14.1%, respectively, and associated with 2140.10 million, 1318.57 million, 1683.03 million, and 2161.22 million US dollars, respectively. Results indicated that we should formulate more reasonable and stringent emission reduction measures to reduce the O3 pollution levels and ensure food security in China.

A novel CMAQ-CNN hybrid model to forecast hourly surface-ozone concentrations 14 days in advance

Issues regarding air quality and related health concerns have prompted this study, which develops an accurate and computationally fast, efficient hybrid modeling system that combines numerical modeling and machine learning for forecasting concentrations of surface ozone. Currently available numerical modeling systems for air quality predictions (e.g., CMAQ) can forecast 24 to 48 h in advance. In this study, we develop a modeling system based on a convolutional neural network (CNN) model that is not only fast but covers a temporal period of two weeks with a resolution as small as a single hour for 255 stations. The CNN model uses meteorology from the Weather Research and Forecasting model (processed by the Meteorology-Chemistry Interface Processor), forecasted air quality from the Community Multi-scale Air Quality Model (CMAQ), and previous 24-h concentrations of various measurable air quality parameters as inputs and predicts the following 14-day hourly surface ozone concentrations. The model achieves an average accuracy of 0.91 in terms of the index of agreement for the first day and 0.78 for the fourteenth day, while the average index of agreement for one day ahead prediction from the CMAQ is 0.77. Through this study, we intend to amalgamate the best features of numerical modeling (i.e., fine spatial resolution) and a deep neural network (i.e., computation speed and accuracy) to achieve more accurate spatio-temporal predictions of hourly ozone concentrations. Although the primary purpose of this study is the prediction of hourly ozone concentrations, the system can be extended to various other pollutants.

Hourly Ozone and PM2.5 Prediction Using Meteorological Data – Alternatives for Cities with Limited Pollutant Information

Using statistical models, the average hourly ozone (O3) concentration was predicted from seven meteorological variables (Pearson correlation coefficient, R = 0.87–0.90), with solar radiation and temperature being the most important predictors. This can serve to predict O3 for cities with real time meteorological data but no pollutant sensing capability. Incorporating other pollutants (PM2.5, SO2, and CO) into the models did not significantly improve O3 prediction (R = 0.91–0.94). Predictions were also made for PM2.5, but results could not reflect its peaks and outliers resulting from local sources. Here we make a comparative analysis of three different statistical predictor models: (1) Multiple Linear Regression (MLR), (2) Support Vector Regression (SVR), and (3) Artificial Neuronal Networks (ANNs) to forecast hourly O3 and PM2.5 concentrations in a mid-sized Andean city (Manizales, Colombia). The study also analyzes the effect of using different sets of predictor variables: (1) Spearman coefficients higher than ± 0.3, (2) variables with loadings higher than ± 0.3 from a principal component analysis (PCA), (3) only meteorological variables, and (4) all available variables. In terms of the O3 forecast, the best model was obtained using ANNs with all the available variables as predictors. The methodology could serve other researchers for implementing statistical forecasting models in their regions with limited pollutant information.

Analysis of surface and vertical measurements of O3 and its chemical production in the NCP region, China

Abstract Ozone has become a major atmospheric pollutant in the North Central Plain (NCP) of China. Comprehensive measurements concerning the vertical profiles and photochemical formation mechanisms of typical O3 pollution episode during August 8–13, 2018 were conducted in the NCP. A maximum hourly ozone concentration of 346 μg/m3 was observed on Aug 10, in which the surface O3 photochemistry was under a strong VOC-limited regime, and the ratio of VOC to NOX ranged from 4:1 to 4:3, with ethylene, toluene, xylene, and propene providing large contributions to O3 chemical production. The vertical measurements of O3, NOX, and the planetary boundary layer (PBL) height suggested that there were three different layers of photochemical O3 production from the surface to 1.1 km. The first layer was from the surface to 200 m, with high NO concentrations leading to a strong conversion between O3 and NO2. The surface O3 concentration was significantly lower than at 220 m during nighttime and early morning, with an average O 3 of approximately 60 μg/m3 and the maximum vertical gradient appearing in the early morning. Diurnal variations of NO2 and N O 2 were significantly inversely correlated with O3 and O 3 . In the second vertical layer between 200 m and 1000 m the value of d[O3]/dt ranged from 10 to 40 μg/m3/h, and the maximum chemical production of O3 occurred at 300 m. The third layer was located outside the PBL height, where O3 precursors with short chemical lifetimes rapidly decreased, producing weak chemical O3 production.

Potential regional air quality impacts of cannabis cultivation facilities in Denver, Colorado

Abstract. The legal commercialization of cannabis for recreational and medical use has effectively created a new and almost unregulated cultivation industry. In 2018, within the Denver County limits, there were more than 600 registered cannabis cultivation facilities (CCFs) for recreational and medical use, mostly housed in commercial warehouses. Measurements have found concentrations of highly reactive terpenes from the headspace above cannabis plants that, when released in the atmosphere, could impact air quality. Here we developed the first emission inventory for cannabis emissions of terpenes. The range of possible emissions from these facilities was 66–657 t yr−1 of terpenes across the state of Colorado; half of the emissions are from Denver County. Our estimates are based on the best available information and highlight the critical data gaps needed to reduce uncertainties. These realizations of inventories were then used with a regulatory air quality model, developed by the state of Colorado to predict regional ozone impacts. It was found that most of the predicted changes occur in the vicinity of CCFs concentrated in Denver. An increase of 362 t yr−1 in terpene emissions in Denver County resulted in increases of up to 0.34 ppb in hourly ozone concentrations during the morning and 0.67 ppb at night. Model predictions indicate that in Denver County every 1000 t yr−1 increase in terpenes results in 1 ppb increase in daytime hourly ozone concentrations and a maximum daily 8 h average (MDA8) increase of 0.3 ppb. The emission inventories developed here are highly uncertain, but highlight the need for more detailed cannabis and CCF data to fully understand the possible impacts of this new industry on regional air quality.

A two decades study on ozone variability and trend over the main urban areas of the São Paulo state, Brazil.

In this paper, we analyze the variability of the ozone concentration over São Paulo Macrometropolis, as well the factors, which determined the tendency observed in the last two decades. Time series of hourly ozone concentrations measured at 16 automated stations from an air quality network from 1996 to 2017 were analyzed. The temporal variability of ozone concentrations exhibits well-defined daily and seasonal patterns. Ozone presents a significant positive correlation between the number of cases (thresholds of 100-160 μg m-3) and the fuel sales of gasohol and diesel. The ozone concentrations do not exhibit significant long-term trends, but some sites present positive trends that occurs in sites in the proximity of busy roads and negative trends that occurs in sites located in residential areas or next to trees. The effect of atmospheric process of transport and ozone formation was analyzed using a quantile regression model (QRM). This statistical model can deal with the nonlinearities that appear in the relationship of ozone and other variables and is applicable to time series with non-normal distribution. The resulting model explains 0.76% of the ozone concentration variability (with global coefficient of determination R1 = 0.76) providing a better representation than an ordinary least square regression model (with coefficient of determination R2 = 0.52); the effect of radiation and temperature are the most critical in determining the highest ozone quantiles.

A data ensemble approach for real-time air quality forecasting using extremely randomized trees and deep neural networks

Six generalized machine learning (ML) ensemble models were developed to predict the real-time hourly ozone concentration of the following day. These models were used to forecast hourly ozone concentrations of the following day for all of 2017 in the city of Seoul, South Korea. To prepare the training dataset, it was referred to observed meteorology and air pollution parameters of the 2014–2016 period. The ensemble models fuse two regression models: a low-ozone peak model and a high-ozone model. For both, extremely randomized trees and deep neural networks were used. A regularization approach was also adopted that adjusts the model toward capturing higher ozone peaks by resampling the training dataset based on the peaks. Adopting the proposed ML ensemble forecasting method over single-model ML techniques as a part of mainstream practice for air quality forecasting will be beneficial for several reasons. For one, the proposed method, which captures daily maximum ozone concentrations during the high-ozone season (April–September), reduces the ozone peak prediction error by 5 to 30 ppb. In addition, compared to station-specific (independent) ML models with more frequent low-ozone values, models are trained with a uniformly distributed dataset, so they are more generalizable in nature. As a result, unlike station-specific models, they retain their accuracy (yearly IOA = 0.84–0.89) in all stations with an IOA increment. Proposed models also make predictions several times faster, requiring only one-time training for predicting an entire station network. Based on a categorical analysis of the training dataset, an algorithm was proposed for selecting the most suitable model for each month. The “best” model further improves the accuracy of both the ML ensemble and individual models by up to 2.4%. This study shows that the ML ensemble modeling approach is a fast, reliable, and robust technique that can benefit environmental decision-makers in urban regions.

A real-time hourly ozone prediction system using deep convolutional neural network

This study uses a deep learning approach to forecast ozone concentrations over Seoul, South Korea for 2017. We use a deep convolutional neural network (CNN). We apply this method to predict the hourly ozone concentration on each day for the entire year using several predictors from the previous day, including the wind fields, temperature, relative humidity, pressure, and precipitation, along with in situ ozone and NO2 concentrations. We refer to a history of all observed parameters between 2014 and 2016 for training the predictive models. Model-measurement comparisons for the 25 monitoring sites for the year 2017 report average indices of agreement (IOA) of 0.84–0.89 and a Pearson correlation coefficient of 0.74–0.81, indicating reasonable performance for the CNN forecasting model. Although the CNN model successfully captures daily trends as well as yearly high and low variations of the ozone concentrations, it notably underpredicts high ozone peaks during the summer. The forecasting results are generally more accurate for the stations located in the southern regions of the Han River, the result of more stable topographical and meteorological conditions. Furthermore, through two separate daytime and nighttime forecasts, we find that the monthly IOA of the CNN model is 0.05–0.30 higher during the daytime, resulting from the unavailability of some of the input parameters during the nighttime. While the CNN model can predict the next 24 h of ozone concentrations within less than a minute, we identify several limitations of deep learning models for real-time air quality forecasting for further improvement.

Ground-level ozone predictions using outlier identification leveraged sample weighted regressors

ABSTRACTGround-level ozone is a pollutant, greenhouse gas, and respiratory irritant which may facilitate skin cancer development and be involved in cardiovascular, respiratory and a range of other diseases. A re-distribution in the hourly ozone concentrations has occurred in the past decades while the interest in obtaining precise methods for the prediction of ozone measures has risen. Weather conditions influence ozone levels, specifically we used maximum temperature per hour, solar radiation per hour, date and hour of the measurement in order to fit prediction models. Weather stations may provide defective data with missing values or incorrect measures which may lead to a decrease in the performance of data driven predictors. This paper proposes a new method that deals with raw data without preprocessing by weighting the effect of automatically detected outliers. The method is evaluated against other traditional outlier removal techniques for a case study in Ponferrada, Spain. Our method yielded great performance for ground-level ozone prediction in simpler and more sophisticated regression techniques, such as linear regression and multi-layer perceptron algorithms.

Seasonal Variation in Surface Ozone and Its Effect on the Winter Wheat and Rice in Nanjing, China

In recent years, surface ozone concentration has been increasing. A high concentration of ozone can affect the growth of crops, and reduce crop yields. In this paper, based on hourly ozone concentration data in the Nanjing area obtained from 2014 to 2016, we analyzed the variation characteristic of ozone concentration and its effect on the production and economic loss of winter wheat and rice. The results shows that the mean concentrations of ozone in 2014, 2015, and 2016 were 62.9, 68.6 and 69.1 μg·m-3, respectively; the ozone concentration and the number of days exceeding the standard gradually increased each year. The order (high to low) of seasonal average ozone concentrations was summer, spring, autumn, and winter. The diurnal variation of ozone concentration in the four seasons showed a single-peak curve, with the peak and valley ozone values appearing at 15:00-16:00 and 07:00-08:00, respectively. The accumulated ozone exposure over a threshold of 40 ppb (AOT40) values during the growing season of winter wheat in 2014, 2015, and 2016 were 10.5, 14.4, and 9.4 μL·L-1·h, respectively. The ozone AOT40 values during the growing season of rice in 2014, 2015, and 2016 were 8.5, 20.0, and 25.6 μL·L-1·h, respectively. At the current ozone level, the impact of surface ozone on winter wheat is higher than that on rice; the range of ozone effect on the yield loss rate of winter wheat was 21.4%-32.8%, and the annual economic loss amounted to 150.766-277.996 million Yuan RMB. The range of ozone effect on the yield loss rate of rice was 8.1%-24.3%, and the annual economic loss amounted to 197.472-680.757 million Yuan RMB.

Predicting hourly ozone concentrations using wavelets and ARIMA models

In recent years, air pollution has been a major concern for its implications on human health. Specifically, ozone (mathrm{O}_{3}) pollution is causing common respiratory diseases. In this paper, we illustrate the process of modeling and prediction hourly mathrm{O}_ {3} pollution measurements using wavelet transforms. We split the time series of mathrm{O}_{3} in daily intervals and estimate scale and wavelet coefficients for each interval by the discrete wavelet transform (DWT) with Haar filter. Subsequently we apply cumulated autoregressive integrated moving average (ARIMA) to estimate the coefficients and forecast their evolution in future intervals. Then the inverse discrete wavelet transform is implemented for the reconstruction of the time series and the forecast in the near future. In order to assess the performance of the proposed methodology, we compare the predictions obtained by the DWT–ARIMA with those obtained by the ARIMA model. Several theoretical results are shown through a simulation study.

Reanalysis of and attribution to near-surface ozone concentrations in Sweden during 1990–2013

Abstract. We have constructed two data sets of hourly resolution reanalyzed near-surface ozone (O3) concentrations for the period 1990–2013 for Sweden. Long-term simulations from a chemistry-transport model (CTM) covering Europe were combined with hourly ozone concentration observations at Swedish and Norwegian background measurement sites using retrospective variational data analysis. The reanalysis data sets show improved performance over the original CTM when compared to independent observations. In one of the reanalyses, we included all available hourly near-surface O3 observations, whilst in the other we carefully selected time-consistent observations. Based on the second reanalysis we investigated statistical aspects of the distribution of the near-surface O3 concentrations, focusing on the linear trend over the 24-year period. We show that high near-surface O3 concentrations are decreasing and low O3 concentrations are increasing, which is reflected in observed improvement of many health and vegetation indices (apart from those with a low threshold). Using the CTM we also conducted sensitivity simulations to quantify the causes of the observed change, focusing on three factors: change in hemispheric background concentrations, meteorology and anthropogenic emissions. The rising low concentrations of near-surface O3 in Sweden are caused by a combination of all three factors, whilst the decrease in the highest O3 concentrations is caused by European O3 precursor emissions reductions. While studying the impact of anthropogenic emissions changes, we identified systematic differences in the modeled trend compared to observations that must be caused by incorrect trends in the utilized emissions inventory or by too high sensitivity of our model to emissions changes.

Prediction of hourly ozone concentrations with multiple regression and multilayer perceptron models

In this work ozone observations of an urban area of the east coast of the Iberian Peninsula, are analyzed. The data set contains measurements from five automatic air pollution monitoring stations (background suburban or traffic urban). The application of multiple linear regression and neural networks models is considered. These models forecast hourly ozone levels for short-term prediction intervals (1, 8, and 24 h in advance). The study period is 2010–2012. The input variables are meteorological observations, ozone and nitrogen oxides concentrations, and daily and weekly seasonal cycles. The performance criteria to evaluate the computations accuracy are the residual mean square error, the mean absolute error, and the correlation coefficient between observations and predictions. These criteria have better results for the 1-h and 24-h predictions in all the locations. The comparison of multiple linear regressions and multilayer perceptron networks indicates that the second approach allows to obtain more accurate forecast for the three prediction intervals.

A statistical modeling approach for air quality data based on physical dispersion processes and its application to ozone modeling

For many complex environmental processes such as air pollution, the underlying physical mechanism usually provides valuable insights into the statistical modeling. In this paper, we propose a statistical air quality model motivated by a commonly used physical dispersion model, called the scalar transport equation. The emission of a pollutant is modeled by covariates such as land use, traffic pattern and meteorological conditions, while the transport and decay of a pollutant are modeled through a convolution approach which takes into account the dynamic wind field. This approach naturally establishes a nonstationary random field with a space–time nonseparable and anisotropic covariance structure. Note that, due to the extremely complex interactions between the pollutant and environmental conditions, the space–time covariance structure of pollutant concentration data is often dynamic and can hardly be specified or envisioned directly. The relationship between the proposed spatial-temporal model and the physics model is also shown, and the approach is applied to model the hourly ozone concentration data in Singapore.