Ground-level Concentrations Research Articles

Trade-driven relocation of ground-level SO2 concentrations across Chinese provinces based on satellite observations.

The influence of trade on ground-level SO2 concentrations in China was evaluated based on multiregional input-output (MRIO) analysis, using the ozone monitoring instrument (OMI) SO2 columns and SO2 profiles from an atmospheric chemical and transport model, MOZART-4. The provincial sum of ground-level SO2 concentrations has a good consistency with the provincial SO2 emissions (R = 0.65, p < 0.01). The provincial SO2 concentrations presented strong spatial variations, with a range of 5.1-50.6μg/m3 and an average of 19.7μg/m3 across China. The international trade increased the SO2 concentrations in all of the provinces and increased the national population-weighted SO2 (PWM-SO2) concentration by 2.9μg/m3. Interprovincial trade within China decreased the ambient SO2 concentrations in Beijing, Tianjin, and Chongqing and the provinces in southeast and central China, but increased SO2 in the remaining provinces of China. In general, interprovincial trade decreased the national PWM-SO2 concentration by 5.3μg/m3.

Evaluation of Source Emissions Dispersion Potential Near a Coastal Village of Maharashtra, India

Industrial emissions are a serious environmental problem worldwide due to particulates and toxic gases. This study aims to generate an activity-specific emission inventory and estimate emissions dispersion extent in the vicinity of the coastal industrial village by simulating the existing coke oven and pellet plant emissions using the steady-state plume model. Continuous air quality monitoring results were compared with the predicted consequential emissions for the year 2018-19. The maximum ground-level concentrations of particulate and gases within the modeling simulation domain were observed at 9005 m away from the center. They were predicted to be 116.39 μg.m-3, 79.14 μg.m-3, 52.97 μg.m-3, and 211.86 μg.m-3. Data analysis showed that air mass transport from the project to the receptor sites resulted in ambient air concentrations higher than those observed in the other sites. Overall predicted results obtained from AERMOD Cloud simulations were shown to have less bias than the measured results. They recommended considering it as appropriate for the prediction of annual average concentration.

Estimation of the atmospheric boundary layer height by means of machine learning techniques using ground-level meteorological data

Aerosols affect air quality, weather and climate through many mechanisms and are dangerous to human health. They are mostly concentrated within the atmospheric boundary layer (ABL) its height is affected by the radiation emitted by the surface, causing turbulence and evolving along the day, influencing the vertical mixing of the air pollutants generated near the surface and therefore, their ground-level concentration from local sources. Lidars have demonstrated their capabilities to study the aerosol vertical distribution and their spatio-temporal evolution can provide very complete information on the ABL dynamics. In this work, machine learning techniques are employed to predict the ABL height. The meteorological variables measured at ground-level are used as features of the algorithm and the ABL height estimated by the STRATfinder algorithm using ceilometer profiles, a small lidar instrument with enhanced characteristics for unassisted continuous operation, are considered the truth in the supervised regression algorithm. The machine learning models allow considering combination of features in the regression algorithm and also allow characterizing the importance of each of the predictors to determine the final result. This property is used to study different boundary layer regimes. The ABL is difficult to study in certain parts of the day due to transitions between atmospheric regimes. In order to improve the performance of the model, each day was divided in four parts (nighttime, morning, daytime and evening). The Madrid ceilometer profile database has been studied for the year 2020, splitting the training datasets for the machine learning algorithm into season and part of the day, and the importance of predictors analyzed. Major influence of temperature and relative humidity is found in most of the situations, but also wind velocity in certain circumstances and pressure. The influence of radiation is small, contrary to expected. The main advantage of the proposed method is that MLHs and ABLHs can be retrieved directly from widely available ground-level meteorological data. Future work will focus on more relevant predictors, as latent heat or turbulence.

Estimating daily ground-level NO2 concentrations over China based on TROPOMI observations and machine learning approach

Nitrogen dioxide (NO2) is an important target for monitoring atmospheric quality. Deriving ground-level NO2 concentrations with much finer resolution, it requires high-resolution satellite tropospheric NO2 column as input and a reliable estimation algorithm. This paper aims to estimate the daily ground-level NO2 concentrations over China based on machine learning models and the TROPOMI NO2 data with high spatial resolution. In this study, four tree-based algorithm machine learning models, decision trees (DT), gradient boost decision tree (GBDT), random forest (RF) and extra-trees (ET), were used to estimate ground-level NO2 concentrations. In addition to considering many influencing factors of the ground-level NO2 concentrations, we especially introduced simplified temporal and spatial information into the estimation models. The results show that the extra-trees with spatial and temporal information (ST-ET) model has great performance in estimating ground-level NO2 concentrations with a cross-validation R2 of 0.81 and RMSE of 3.45 μg/m3 in test datasets. The estimated results for 2019 based on the ST-ET model achieves a satisfactory accuracy with a cross-validation R2 of 0.86 compared with the other models. Through time-space analysis and comparison, it was found that the estimated high-resolution results were consistent with the ground observed NO2 concentrations. Using data from January 2020 to test the prediction power of the models, the results indicate that the ST-ET model has a good performance in predicting ground-level NO2 concentrations. Taking four ground-level NO2 concentrations hotspots as examples, the estimated ground-level NO2 concentrations and ground-based observation data during the coronavirus disease (COVID-19) pandemic were lower compared with the same period in 2019. The findings offer a solid solution for accurately and efficiently estimating ground-level NO2 concentrations by using satellite observations, and provide useful information for improving our understanding of the regional atmospheric environment.

Evaluation of vertical and horizontal distribution of particulate matter near an urban roadway using an unmanned aerial vehicle.

Measurement of traffic emissions has gained a lot of interest in recent times due to its contribution to urban pollution. This paper reports the outcome from an unmanned aerial vehicle (UAV) based measurement of PM concentration near an urban roadway at Kolkata, India. A total of 54 flights were carried out for simultaneous measurements of PM1, PM2.5 and PM10 mass concentration and meteorological parameters in vertical as well as in horizontal direction. Results for the vertical flight up to 100 m showed that the PM1, PM2.5 and PM10 concentrations at higher altitudes are less (mean; 24.6, 39.9 and 103.8 μg m-3) compared to the respective ground level concentrations (mean; 26.3, 50.4 and 201.9 μg m-3). For all the three particle sizes, the majority of the cases of higher PM concentration at higher altitudes happened during the evening flight. Low mixing height and low wind speed are suggested to be the reasons for the poor dispersion of pollutants in the evening. While there was a 7-10% fall of fine particles (PM1 and PM2.5) mass concentrations up to 90 m away from the road, no trend could be seen for PM10. The random forest model to predict the UAV/Ground concentration ratio showed high accuracy (R2 = 0.82-0.95) for all three particle sizes. This is an important finding from this study, which shows how UAV measurement data can be used to generate models that can predict the higher altitude concentrations from the ground based measurements.

Estimating monthly global ground-level NO2 concentrations using geographically weighted panel regression

Predicting long-term ground-level nitrogen dioxide (NO2) is important globally to support environmental and public health research and to provide information to governments and society for air pollution control policies. The ozone monitoring instrument (OMI), onboard Aura Satellite, detects monthly global tropospheric column amounts (TrCA) of NO2 molecules. However, the relationship between the ground-level NO2 concentration and TrCA of NO2 molecules remains elusive because NO2 molecules in the air are not evenly distributed vertically. We use geographically weighted panel regression (GWPR) to examine the relationship between satellite-derived data, measured ground-level NO2 concentrations, and several controlling meteorological variables from January 2015 to October 2021. The GWPR can analyze unbalanced panel data and capture the spatial variability of the relationship. Based on the GWPR estimation, 82 monthly global ground-level NO2 concentrations are predicted from January 2015 to October 2021. The GWPR is reliable, as indicated by the 10-fold cross-validation. The accuracy of the raster prediction of global ground-level NO2 from January 2015 to October 2021 is 69.61%. The coefficient of correlation, root mean square error and mean absolute error between globally predicted and measured ground-level NO2 are 0.838, 7.84 μg/m3 and 4.07 μg/m3, respectively, while the mean of globally measured ground-level NO2 is 19.47 μg/m3. Overall, this research provides critical basic data to environmental and public health science and valuable information for governments and societies to make more reasonable policies.

Concentration-Response Functions as an Essence of the Results from Lags.

Among various aspects of environmental epidemiology, one is to assess the relationships between ambient air pollution and health outcomes. The goal of this work is to estimate the associations in the form of the parametric concentration–response functions (C-RF). Various forms of the C-RFs are proposed in this short-term health effect study. Emergency department (ED) visits for all respiratory health problems are analyzed as an illustrative example. A case-crossover (CC) technique is applied as a study design. Daily cases are organized as daily counts by the same day of the week in one common month. A conditional Poisson regression is used in the constructed statistical models. Temperature and relative humidity are included in the statistical models in the form of natural splines. Ground-level ozone concentration is considered an exposure. Ozone concentration values are transformed and submitted to the statistical models. The parameters of the transformation are determined by using the goodness of fit criterion. Counts of ED visits are analyzed in relation to a sequence of lagged exposure to ozone. The C-RF shapes are constructed for each individual lag. In a final step, the set of the estimated C-RF shapes is used to create a pooled C-RF shape. The results are positive and statistically significant for nine lagged exposures, from 0 to 8 days. The following relative risks (RR) were estimated from the constructed C-RFs at 30 ppb concentration of ozone: RR = 1.0531 (95% confidence interval: 1.0231, 1.0718), 1.0462 (1.0253, 1.0677), and 1.0387, (1.0240, 1.0531), realizing the CC method, CC method + transformation, and CC method + flexible transformation, respectively. The pooled C-RF shape gives a summary of the associations between ED visits for respiratory conditions and ambient ozone. The estimated shapes indicate lower air health effects than the standard CC methods. Among three considered statistical models, the CC method + flexible transformation is the most appropriate to use according to the goodness of fit criterion.

Simulation of thermal power plant source contribution to ambient air concentration in Cam Pha City, Quang Ninh province using AERMOD dispersion model

Cam Pha, the home of three major thermal power plants including Cam Pha, Mong Duong I and Mong Duong II, is one of the most important industrial cities in the North of Vietnam. Air pollution due to stacks emission is the biggest problem threatening Cam Pha City's sustainable development. In this study, the AERMOD modeling system was used to evaluate the impact of the stack emission by the thermal power plants on the ambient atmospheric environment. The maximum 1-HR, 24-HR, 99th percentile and annual average concentrations of TSP, SO2 and NO2 were simulated within the 40x40 km domain of 100x100 m grid spacing with the hourly meteorological data taken from 2018 to 2020. Air dispersion simulation is performed on the observed background gas concentration of the everyday environment. Hourly emission data of 10 primary stack sources of 3 factory groups were used as input data. The simulated spatial distribution of gases indicates the strong fluence of the mountainous topography on the dispersion of stack emission. Results also revealed that the maximum short-term stack emission at ground-level concentrations of SO2 and NO2 are much higher than the national standard, thus raising the risk of severe pollution. TSP pollution is less severe than SO2 and NO2 but still at a dangerous level. Since Cam Pha locates by the East Sea with the prevailing wind is heading northeast and east directions. The annual average concentrations of these pollutants indicate that the high terrain areas at the south and northwest of Cam Pha City, which block the flow of the stack emission, are the most affected regions by exhausted gases from industrial stacks.

Evaluation of a Revised AERMOD Treatment of Plume Dispersion in the Daytime Elevated Stable Layer

Advanced dispersion models such as AERMOD specifically address the portion of a plume emitted in convective conditions that is sufficiently buoyant to rise into the stable layer above the elevated inversion. This portion of the plume mass is often referred to as the “penetrated plume” because that plume component breaks through the elevated inversion and penetrates into the stable layer aloft. A pre-mature mixing of the penetrated plume to the ground has been identified in the current formulation of AERMOD, which is the USEPA-preferred short-range dispersion model and used in several other countries. This behavior has been observed based on data from field studies where the model is found to overpredict ground-level concentration events due to the penetrated plume component, with the timing of these peak predictions too early in the day. A proposed update to AERMOD to address the penetrated plume issue (referred to as “HBP” for modifications particularly important for “highly buoyant plume”) is documented and evaluated in this manuscript. The revised approach involves a check on the convective mixing height for the current hour as well as the next hour to determine how much of the penetrated plume has been captured by the convective boundary layer by the end of the current hour. The amount of the penetrated plume mass that is allowed to mix to the ground in the HBP modifications depends upon the result of this calculation. The HBP modification has been evaluated as an update to AERMOD for 3 databases along with a sensitivity analysis of the effects of the HBP changes on a variety of stack heights and buoyancy fluxes. The findings of the evaluation indicate that the HBP changes to AERMOD result in reduced overprediction tendencies. “Implications Statement” for the HBP paper being submitted to JAWMA A proposed enhancement to AERMOD to address a pre-mature mixing of penetrated plume material to the ground has been performed by implemented and evaluated by the authors. The enhancement, referred to as the highly buoyant plume (HBP) is based on work developed by Dr. Jeffrey Weil. HBP is designed to better characterize the penetrated plume behavior in the model such that it aligns more closely with observations based on data from field studies.

Assessment of NO2 population exposure from 2005 to 2020 in China.

Nitrogen dioxide (NO2) is a major air pollutant with serious environmental and human health impacts. A random forest model was developed to estimate ground-level NO2 concentrations in China at a monthly time scale based on ground-level observed NO2 concentrations, tropospheric NO2 column concentration data from the Ozone Monitoring Instrument (OMI), and meteorological covariates (the MAE, RMSE, and R2 of the model were 4.16 µg/m3, 5.79 µg/m3, and 0.79, respectively, and the MAE, RMSE, and R2 of the cross-validation were 4.3 µg/m3, 5.82 µg/m3, and 0.77, respectively). On this basis, this article analyzed the spatial and temporal variation in NO2 population exposure in China from 2005 to 2020, which effectively filled the gap in the long-term NO2 population exposure assessment in China. NO2 population exposure over China has significant spatial aggregation, with high values mainly distributed in large urban clusters in the north, east, south, and provincial capitals in the west. The NO2 population exposure in China shows a continuous increasing trend before 2012 and a continuous decreasing trend after 2012. The change in NO2 population exposure in western and southern cities is more influenced by population density compared to northern cities. NO2 pollution in China has substantially improved from 2013 to 2020, but Urumqi, Lanzhou, and Chengdu still maintain high NO2 population exposure. In these cities, the Environmental Protection Agency (EPA) could reduce NO2 population exposure through more monitoring instruments and limiting factory emissions.

Spatiotemporal Variations in Summertime Ground-Level Ozone around Gasoline Stations in Shenzhen between 2014 and 2020

Ground-level ozone has become the primary air pollutant in many urban areas of China. Oil vapor pollution from gasoline stations accelerates the generation of ground-level ozone, especially in densely populated urban areas with high demands for transportation. An accurate spatiotemporal distribution of ground-level ozone concentrations (GOCs) around gasoline stations is urgently needed. However, urban GOCs vary sharply over short distances, increasing the need for GOCs at a high-spatial resolution. Thus, a high-spatial resolution (i.e., 1 km) concentration retrieval model based on the GLM and BME method was developed to obtain the daily spatiotemporal characteristics of GOCs. The hourly ozone records provided by the national air quality monitoring stations and multiple geospatial datasets were used as input data. The model exhibited satisfactory performance (R2 = 0.75, RMSE = 10.86 µg/m3). The derived GOCs show that the ozone levels at gasoline stations and their adjacent areas (1~3 km away from the gasoline stations) were significantly higher than the citywide average level, and this phenomenon gradually eased with the increasing distance from the gasoline stations. The findings indicate that special attention should be given to the prevention and control of ground-level ozone exposure risks in human settlements and activity areas near gasoline stations.

Estimation of particulate matter pollution using WRF-Chem during dust storm event over India

This study estimates the ground-level PM10 concentration by effectively combining the Aerosols Optical Depth (AOD) from Moderate Resolution Imaging Spectroradiometer (MODIS) satellite retrievals and Weather Research and Forecasting Model coupled with Chemistry (WRF-Chem). The model simulates typical dust storm events 17th-22nd April 2010 and 05th–10th May 2010 which has severely affected air quality in North and Northwestern India. The satellite retrievals shows high AOD (>1.0) over Indo Gangetic Plains and nearby Thar Desert. The model captures the spatial pattern of AOD very well however, it underestimates high aerosol loading in comparision to MODISAOD. The modeled AOD (MODELAOD) shows an underestimation by 37% with MODISAOD over the study region. Therefore, the WRF-Chem model Particulate Matter (PM10) and MODELAOD are scaled using satellite MODISAOD to provide a better estimation of the particulate pollution. The results shows better estimation, trend and correlation (R = 0.83) of the PM10 with hourly observations at Delhi monitoring station and a Mean Bias (MB) of 61 μg/m3 during the satellite overpass time. The comparison of estimated PM with daily averaged observations of PM10 from Central Pollution Control Board (CPCB) at stations of Jaipur, Jodhpur, Kota, and Delhi showed a strong agreement with an correlation of (R) of 0.81, 0.70, 0.77 and 0.78, respectively.

Quantify the Effect of Chemical Transformation on Ground-level Concentrations of Contaminants in Ghana Using the CALPUFF Modelling System

Quantify the Effect of Chemical Transformation on Ground-level Concentrations of Contaminants in Ghana Using the CALPUFF Modelling System

Effects of vertical ship exhaust plume distributions on urban pollutant concentration – a sensitivity study with MITRAS v2.0 and EPISODE-CityChem v1.4

Abstract. The modeling of ship emissions in port areas involves several uncertainties and approximations. In Eulerian grid models, the vertical distribution of emissions plays a decisive role for the ground-level pollutant concentration. In this study, model results of a microscale model, which takes thermal plume rise and turbulence into account, are derived for the parameterization of vertical ship exhaust plume distributions. This is done considering various meteorological and ship-technical conditions. The influence of three different approximated parameterizations (Gaussian distribution, single-cell emission and exponential Gaussian distribution) on the ground-level concentration are then evaluated in a city-scale model. Choosing a Gaussian distribution is particularly suitable for high wind speeds (&gt;5 m s−1) and a stable atmosphere, while at low wind speeds or unstable atmospheric conditions the plume rise can be more closely approximated by an exponential Gaussian distribution. While Gaussian and exponential Gaussian distributions lead to ground-level concentration maxima close to the source, with single-cell emission assumptions the maxima ground-level concentration occurs at a distance of about 1500 m from the source. Particularly high-resolution city-scale studies should therefore consider ship emissions with a suitable Gaussian or exponential Gaussian distribution. From a distance of around 4 km, the selected initial distribution no longer shows significant differences for the pollutant concentration near the ground; therefore, model studies with lower resolution can reasonably approximate ship plumes with a single-cell emission.

Long-term variations of ground-level NO2 concentrations along coastal areas in China

The long-term trends of the ground-level NO2 concentrations over coastal areas in China were analysed from 2007 to 2019 in this study, based on the measurements from Ozone Monitoring Instrument (OMI) and the simulations from the Model for Ozone and Related Chemical Tracers (MOZART)-4. Multiple Gaussian functions were used to simulate the atmospheric profiles of NO2 concentrations from the MOZART-4, and the ratio of the ground-level concentrations to the columns estimated from this Gaussian function was used to convert the OMI NO2 columns to that at the ground level. The estimation results showed that the ground-level NO2 concentrations were well consistent with those of ground measurements in 2017 (the correlation coefficients ranging from 0.84 to 0.87 in the four seasons). The NO2 concentrations presented an increasing rate of 0.6 μg/m3/yr during 2007–2011 and then a decreasing rate of 0.5 μg/m3/yr until 2019. Most areas in the China Sea suffered from the maximum NO2 concentrations during 2011–2013. The temporal variations of NO2 over coastal lands were consistent with those over the ocean during 2007–2016, but they presented different after the implementation of coastal emission control area (ECA) policies in 2016. The ECA policies significantly decreased the NO2 concentrations in the Yangtze River Delta (the YRD) and the Bohai Rim, and they inhibited the increase of NO2 in the Pearl River Delta (the PRD). These results might be helpful in understanding the current situation of ground-level NO2 pollution over oceans and coastal land in China and provide guidance for further development of ECA policies.

Forecasting ground-level ozone concentration levels using machine learning

Ground-level ozone (GLO) has been widely recognized as a critical air pollutant that has the potential to induce various adverse environmental and health effects. To eliminate its hazardous impacts, the development of an accurate and effective approach to forecast the upcoming pollution concentration levels is in urgent need. Recent studies show that machine learning algorithms have excellent abilities in ground-level ozone concentration forecasting, however many of their forecasting models do not consider the contributing effects on meteorological measurements and traffic situations which were also identified as potential influencing factors to ground-level ozone concentration by some previous research. In the meantime, most of the existing models target short-term forecasting with rough temporal resolutions, such as daily and weekly scales. This paper aims to propose a methodology that can provide long-term GLO concentration forecasting with a high temporal resolution. To achieve this, a frequent pattern mining approach is utilized to analyze the local intercorrelation between GLO concentration and contributing factors such as meteorological parameters and transportation situations. Then, a series of machine learning algorithms were identified to forecast the ground-level ozone concentration levels using traffic and meteorological measurements data. A case study was conducted in the Houston region with 10 years of historical measurements, each of the historical ground-level ozone concentration records is associated with a series of meteorology and traffic situation parameters. Data from 2010 to 2019 was used to select and train the machine learning models, and data from 2020 was used to perform the final validation and evaluation. Results show that the extreme gradient boost (XGBoost) machine learning algorithm provides the most accurate prediction of the hourly ground-level ozone concentration on a yearly scale, which shows the annually forecasting ability and the robustness of the model built. The proposed approach could be applied to other similar regions and other critical air pollutants that are also influenced by transportation and meteorological factors.

Geostationary satellite-derived ground-level particulate matter concentrations using real-time machine learning in Northeast Asia

Rapid economic growth, industrialization, and urbanization have caused frequent air pollution events in East Asia over the last few decades. Recently, aerosol data from geostationary satellite sensors have been used to monitor ground-level particulate matter (PM) concentrations hourly. However, many studies have focused on using historical datasets to develop PM estimation models, often decreasing their predictability for unseen data in new days. To mitigate this problem, this study proposes a novel real-time learning (RTL) approach to estimate PM with aerodynamic diameters of <10 μm (PM10) and <2.5 μm (PM2.5) using hourly aerosol data from the Geostationary Ocean Color Imager (GOCI) and numerical model outputs for daytime conditions over Northeast Asia. Three schemes with different weighting strategies were evaluated using 10-fold cross-validation (CV). The RTL models, which considered both concentration and time as weighting factors (i.e., Scheme 3) yielded consistent improvement for 10-fold CV performance on both hourly and monthly scales. The real-time calibration results for PM10 and PM2.5 were R2 = 0.97 and 0.96, and relative root mean square error (rRMSE) = 12.1% and 12.0%, respectively, and the 10-fold CV results for PM10 and PM2.5 were R2 = 0.73 and 0.69 and rRMSE = 41.8% and 39.6%, respectively. These results were superior to results from the offline models in previous studies, which were based on historical data on an hourly scale. Moreover, we estimated PM concentrations in the ocean without using land-based variables, and clearly demonstrated the PM transport over time. Because the proposed models are based on the RTL approach, the density of in-situ monitoring sites could be a major uncertainty factor. This study identified that a high error occurred in low-density areas, whereas a low error occurred in high-density areas. The proposed approach can be operated to monitor ground-level PM concentrations in real-time with uncertainty analysis to ensure optimal results.

Estimation of high spatial resolution ground-level ozone concentrations based on Landsat 8 TIR bands with deep forest model

In recent years, China has been facing severe ozone (O3) pollution, which poses a remarkable threat to human health. Most estimation methods only provide ozone products at a relatively coarse resolution, such as 5 km, but high-resolution ozone data are essential for ozone pollution prevention and control. To this end, we proposed a new framework for estimating ozone concentrations at 300 m resolution in China based on Landsat 8 infrared (IR) bands and meteorological data using a deep forest (DF) model. DF combines the excellent performance of tree integration with the expressive power of hierarchical distributed representations of neural networks. The accuracy and mapping results of DF are considerably better than some widely used machine learning methods (generalized regression neural regression network and random forest). The sample-based cross-validation (CV), station-based CV, time-based CV, and extrapolation validation show that the estimations of DF are in high agreement with the station observations with determination coefficient values of 0.938, 0.926, 0.687, and 0.660, respectively. The proposed method was used to analyze the spatial and temporal ozone variations at fine scales in three typical Chinese cities (Beijing, Wuhan and Guangzhou), where the mean ozone concentrations during the polluted season are consistent with the land use and urban heat island distribution. The rationality of ozone estimates was verified, and the advantages of high-resolution mapping was demonstrated by comparing the monitoring data from municipal controlling stations in Beijing, 10 km ozone products, and satellite images. Our product can represent spatial details and locate local pollution sources, such as temples. The proposed method has important implications for the fine-scale monitoring of ozone pollution.

Transport modelling of pollutants emitted from Guinea Savannah vegetation fire

ABSTRACT The Sub-Saharan African region suffers from a high concentration of pollutants from savannah fires, which adversely affect the environment and human health. This study aims to predict the ground-level concentrations (GLC) of pollutants emitted during savannah fires and evaluate their extent of dispersion in the atmosphere. The American Meteorological Society/United States Environmental Protection Agency Regulatory Model (AERMOD) was used to predict pollutant concentrations under different scenarios in Nigeria. The model suggested that the GLC of the 24-hr averaging of PM2.5 exceeded the World Health Organization (WHO) Guidelines. The results can be a basis for mitigation strategies and control of emissions from savannah fires.

Photocatalytic Cementitious Materials to Reduce Air Pollution: Review

Oxides of nitrogen (NOx) are highly reactive and major air pollutants generated from the vehicular emission and burning of fossil fuels in industries. These pollutants will create tropospheric ozone, formation of urban smog and occurrence of acid rain, etc. A potential solution for this would be heterogeneous photocatalysis. Using photocatalysts such as titanium dioxide (TiO2) inorganic and organic matter present in the air, such as NOx and VOCs can be adsorbed and oxidized by the action of ultraviolet (UV) light (sunlight). This technology is promising for cities with highly congested areas having high ground-level pollutant concentrations. Self-cleaning and air-purification coating with a super-hydrophilic photocatalyst (TiO2) coated on buildings can bring down the NOx concentration in the air. In addition, the incorporation of TiO2 in the form of nano-particles into cementitious materials will enhance their mechanical properties. This study evaluates and highlights the applications of nano titanium dioxide and its effects on the fresh and hardened characteristics of the cementitious composites.