Distribution Of Ozone Concentration Research Articles

Detection of Factors Affecting the Spatial Distribution of Ozone in Summer over China Based on Geodetector

To study the factors affecting the spatial distribution of ozone concentration in different regions of China, based on the summer ozone monitoring data of 2019 and three influencing factors, namely, main meteorological, direct emission, and socio-economic factors, the influence degree of various factors on the spatial differentiation of ozone was quantified by using the Geodetector method and the comparison between regions was conducted. The results showed that the spatial differentiation of ozone concentration in summer 2019 was most affected by the main meteorological factors and the average degree of influence reached 0.277. Relative humidity was the dominant meteorological factor and the influence degree reached 0.415. The degree of influence of meteorological factors increased from northeast and western regions to central regions. The influence of social and economic factors increased from north to south, except for the low plateau climate area. The analysis on the interaction of two factors revealed that the average wind speed of meteorological factors had the greatest impact on other factors and large regional differences were present. Among socio-economic factors, population density had the greatest impact on other factors and was the key factor driving the regional differentiation of other factors. These results are helpful to understand the dominant factors of spatial distribution of ozone concentration in different regions of China and have guiding importance for the prevention and control of ozone pollution.

Numerical investigation of turbulent mass transfer processes in turbulent fluidized bed by computational mass transfer

Turbulent fluidized bed possesses a distinct advantage over bubbling fluidized bed in high solids contact efficiency and thus exerts great potential in applications to many industrial processes. Simulation for fluidization of fluid catalytic cracking (FCC) particles and the catalytic reaction of ozone decomposition in turbulent fluidized bed is conducted using the Eulerian–Eulerian approach, where the recently developed two-equation turbulent (TET) model is introduced to describe the turbulent mass diffusion. The energy minimization multi-scale (EMMS) drag model and the kinetic theory of granular flow (KTGF) are adopted to describe gas–particles interaction and particle–particle interaction respectively. The TET model features the rigorous closure for the turbulent mass transfer equations and thus enables more reliable simulation. With this model, distributions of ozone concentration and gas–particles two-phase velocity as well as volume fraction are obtained and compared against experimental data. The average absolute relative deviation for the simulated ozone concentration is 9.67% which confirms the validity of the proposed model. Moreover, it is found that the transition velocity from bubbling fluidization to turbulent fluidization for FCC particles is about 0.5 m·s–1 which is consistent with experimental observation.

Estimation of Near-surface Ozone Concentration in the Beijing-Tianjin-Hebei Region Based on XGBoost-LME Model

High spatiotemporal resolution data on near-surface ozone concentration distribution is of great significance for monitoring and controlling atmospheric ozone pollution and improving the living environment. Using TROPOMI-L3 NO2, HCHO products, and ERA5-land high-resolution data as estimation variables, an XGBoost-LME model was constructed to estimate the near-surface ozone concentration in the Beijing-Tianjin-Hebei Region. The results showed that： ① Through correlation analysis, surface 2 m temperature （T2M）, 2 m dewpoint temperature （D2M）, surface solar radiation downwards （SSRD）, tropospheric formaldehyde （HCHO）, and tropospheric nitrogen dioxide （NO2） were important factors affecting the near-surface ozone concentration in the Beijing-Tianjin-Hebei Region. Among them, T2M, SSRD, and D2M had strong correlations, with correlation coefficients of 0.82, 0.75, and 0.71, respectively. ② Compared with that of other models, the XGBoost-LME model had the best performance in terms of various indicators. The ten-fold cross-validation evaluation indicators R2, MAE, and RMSE were 0.951, 9.27 μg·m-3, and 13.49 μg·m-3, respectively. At the same time, the model performed well at different time scales. ③ In terms of time, there was a significant seasonal difference in near-surface ozone concentration in the Beijing-Tianjin-Hebei Region in 2019, with the concentration changing in the order of summer > spring > autumn > winter. The monthly average ozone concentration in the region showed an inverted "V" trend, with a slight increase in September. The highest value occurred in July, whereas the lowest value occurred in December. In terms of spatial distribution, the near-surface ozone concentrations in the Beijing-Tianjin-Hebei Region during the months of February and March were generally at the same levels. In January, November, and December, there was a relatively insignificant trend of higher concentrations in the north and lower concentrations in the south. For the remaining months, the spatial distribution of near-surface ozone concentrations in this area predominantly exhibited a pattern of higher concentrations in the south and lower concentrations in the north. High-value areas were predominantly found in the plain regions of the southern part with lower altitudes, dense population, and higher industrial emissions； low-value areas, on the other hand, were primarily located in mountainous areas of the northern part with higher altitudes, sparse population, higher vegetation coverage, and lower industrial emissions.

Spatiotemporal Changes in and Forces Driving Ozone Concentration in the Beijing–Tianjin–Hebei (Jing–Jin–Ji) Region from 2015 to 2022

This study aims to explore the factors affecting variation in ozone concentration, using ozone concentration monitoring data from the Beijing–Tianjin–Hebei (Jing–Jin–Ji) region from 2015 to 2022. The characteristics of the spatial and temporal variation in ozone concentration, as well as the impact of meteorological and land-use types on this variation, were investigated. Seasonally, ozone concentrations in the Jing–Jin–Ji region were highest in summer, followed by spring and autumn, and were lowest in winter. Spatially, a characteristic trend of lower concentrations in urban centers and higher concentrations in suburban areas was found. From the results of correlation analysis and a stepwise regression model, temperature and solar radiation were found to be the primary factors affecting seasonal variations in ozone concentration, explaining 67.9% of the variance. Additionally, the results of geographically weighted regression models confirmed the spatial heterogeneity of the impact of temperature and solar radiation on ozone concentration. In urban centers, such as Beijing and Tianjin, temperature and solar radiation more strongly influenced ozone concentration. To determine the factors driving this spatial heterogeneity, a geodetector was employed to analyze the impact of various geographical factors on the spatial distribution of ozone concentration. In winter, the impact of various factors of urbanization, such as lighting index, population density, and construction land, as well as air quality factors (PM2.5 and NO2), on ozone concentration was stronger, with q values greater than 0.2. However, in autumn, the influence of these factors on ozone concentration weakened. Furthermore, the results of the two-factor interaction detection analysis showed that the q values for the interaction between urban and air quality factors ranged between 0.40 and 0.47, which were substantially higher than those for other interactions. This further confirmed the strong influence of urban activities on ozone concentration.

Mass transfer model based on the spatial superposition assumption for simulating the catalytic ozone decomposition in downer reactor

A mass transfer model based on the spatial superposition assumption is proposed in this work to better reflect the effect of heterogeneous structure on the mass transfer behavior of the downer. Attributing to the special assumption of this model, fewer concentration parameters are required compared with the classic multi-scale method, which largely saves the calculation consumption. The simulation results of this model also could be in good agreement with the experimental data. Additionally, the calculated mass transfer coefficients are in the range from 10−3 to 10−1, which are approximately three orders of magnitude lower than that of the homogeneous structure. It is proved that the existence of clusters would seriously affect the gas-solid mass transfer effectiveness, thereby deteriorating the reaction rate. By simulating the distribution of ozone concentration in the catalytic ozone decomposition, it can be found that the mass transfer near the inlet region plays the dominant role while along the flow direction, the influence of reaction process becomes stronger than that of the mass transfer process and gradually turns to control the whole decomposition process. These results indicate that the present model is useful and could be applied for the simulation of mass transfer process in other gas-solid fluidized systems with heterogeneous structures.

Assessing uncertainty and heterogeneity in machine learning-based spatiotemporal ozone prediction in Beijing-Tianjin- Hebei region in China

Accurate prediction of spatiotemporal ozone concentration is of great significance to effectively establish advanced early warning systems and regulate air pollution control. However, the comprehensive assessment of uncertainty and heterogeneity in spatiotemporal ozone prediction remains unknown. Here, we systematically analyze the hourly and daily spatiotemporal predictive performances using convolutional long short term memory (ConvLSTM) and deep convolutional generative adversarial network (DCGAN) models over the Beijing-Tianjin-Hebei region in China from 2013 to 2018. In extensive scenarios, our results show that the machine learning-based (ML-based) models achieve better spatiotemporal ozone concentration prediction performance with multiple meteorological conditions. A further comparison to the air pollution model-Nested Air Quality Prediction Modelling System (NAQPMS) and monitoring observations, the ConvLSTM model demonstrates the practical feasibility of identifying high ozone concentration distribution and capturing spatiotemporal ozone variation patterns at a high spatial resolution (here 15 km × 15 km).

Classification of ozone pollution and analysis of meteorological factors in the Yangtze River Delta

ABSTRACT Serious regional ozone (O3) pollution often plagues the Yangtze River Delta (YRD). The formation mechanism of these regional pollution events, including the meteorological and emission factors leading to these pollution events and how to affect the distribution of O3, still needs further research and exploration. In this study, we first define the standard of O3 regional pollution in the YRD, and then select 248 regional pollution cases from 2015 to 2020 according to the defined standard. For the pollution cases in pollution months (May and June), PCT (principal component analysis in T-mode) classification method is used to classify the ozone concentration distribution in YRD area. The regional distribution of the O3 concentrations in the YRD is divided into five types, and the overall type (Type 1) accounts for 15%, which is related to the control of YRD area by high-pressure center. Under the control of high pressure, the weather is sunny with the high temperature, and this weather condition is favorable for ozone generation and intercity transmission, causing extensive pollution. The double center type (Type 2) accounts for 8%. This type of YRD is controlled by the front of the high pressure (the high-pressure center is located in North China), and the weather in the middle and north is conducive to the generation and transmission of O3. Inland type (Type 3) accounts for 24%. The main body of this type of high pressure is located in Mongolia. The easterly wind in YRD area is conducive to the inland transmission of O3 precursors. The northern coastal type (Type 4) accounts for 44%. This type of YRD area is mainly controlled by the weak pressure field. The weather in the northern coastal area is sunny and the solar radiation for a long time is conducive to the formation of O3. The southern coastal type (Type 5) accounts for 10%, the solar radiation is strong in the southern region mainly under the influence of the post-offshore high pressure. This study provides new insights into the relationship between O3 pollution distribution types and atmospheric circulation in YRD area, and reveals the difference of potential meteorological impacts of different O3 pollution distribution types.

Numerical evaluation of the ozonation process in a hollow fibre membrane contactor

This work presents comprehensive mathematical modelling employed to describe the ozone (O3) mass transfer in the hollow fibre membrane contactor (HFMC) system. Using the model, impacts of operating parameters and membrane specification such as gas phase and liquid flow rates, porosity-to-tortuosity and membrane length on the dissolved ozone at the reactor outlet as well as ozone concentration distribution were simulated. Also, experimental data were used to validate the created simulation model and it was observed that there is reasonable agreement between experimental results and modelling output in terms of dissolved ozone at the reactor outlet at various water flow rate and initial ozone concentration. Based on the simulation outputs, the gas flow rate as well as porosity (ε)-to-tortuosity (τ) do not have impact on the dissolved ozone at reactor outlet while there was slight increase in the ozone outlet concentration with the enhancement of membrane length. Furthermore, the main resistance for ozone mass transfer was liquid phase and it was indicated that the gas phase and membrane subdomain resistance is lower for ozone mass transfer due to higher ozone diffusion coefficient in the gas phase.

Ground-level ozone in the Mekong Delta region: precursors, meteorological factors, and regional transport.

The Mekong Delta region (MDR), also known as Vietnam's rice bowl, produced a bountiful harvest of about 23.8 million tons in 2020, accounting for 55.7% of the country's total production, providing food security for 20% of the world population. With the rapid pace of industrialisation and urbanisation, the concentration of ozone in the lower atmosphere has risen to a level that reduces crop yields, especially rice, and is therefore the subject of research. This study aims to simulate the spatiotemporal distribution of ground-level ozone in the area and evaluate the impact of precursor emissions and meteorological factors on the spatiotemporal distributions of ozone concentrations. The study area was divided into seven zones, including six agro-ecological zones (AEZs) and one low-mountainous area, mainly to clarify the role of emissions in each AEZ. The simulation results showed that ground-level O3 in the MDR ranged from 40.39 to 52.13µg/m3. In six agro-ecological zones, the average annual ground-level O3 concentration was relatively high and was the highest in zone 6 (CPZ) and zone 3 (LXZ) with values of 96.18µg/m3 (exceeding 1.60 times the WHO Guidelines 2021) and 94.86µg/m3 (exceeding 1.58 times the WHO Guidelines 2021), respectively. In each zone, the annual average O3 concentration tended to gradually increase from the inner delta to coastal areas. Two types of precursors, NOx and NMVOCs, are the main contributors to O3 pollution, with the largest contribution coming from zone 1 (FAZ) with 91.5 thousand tons of NOx/year and 455.2 thousand tons of NMVOCs/year. Among the meteorological factors considered, temperature (T), relative humidity (RH), and surface pressure (P) were the three main factors that contributed to the increase in ground-level ozone. The spatio-temporal distribution of ground-level O3 in the MDR was influenced by emission precursors from different zones as well as meteorological factors. The present results can help policy-makers formulate plans for agro-industrial development in the entire region.

CPFD simulation of cluster effect on mass transfer and reaction in downer with FCC particles

In this work, a cluster-based mass transfer and reaction model was proposed according to large number of simulation results by conducting ozone decomposition inside spherical cluster, which is more realistic compared with the conventional homogeneous description of mass transfer and chemical reaction process. Then, this model was coupled with three-dimensional Computational Particle Fluid Dynamics (CPFD) approach to simulate the ozone decomposition in pilot-scale gas-solids downer. According to the comparison between simulation results and experiment measurements, it was found that the model sufficiently considering the clustering effects on the hydrodynamics and chemical reaction predicted better ozone concentration distribution than the model based on homogeneous assumption. Meanwhile, the comprehensive model with consideration of cluster could accurately predict the gas-solids flow behaviors and reaction characteristics with different particle properties under different operating conditions. Finally, the effects of operating conditions such as superficial gas velocity and solids circulating flux on the hydrodynamics and reaction characteristics inside downer were fully analyzed on the basis of the accurate model predictions.

Spatiotemporal Variations and Influent Factors of Tropospheric Ozone Concentration over China Based on OMI Data

Spatiotemporal distribution of atmospheric pollutants has been a hot topic in the field of environmental science in recent years. Rapid economic development, urbanization, and industrialization have resulted in a significant increase in ozone emissions, and China is facing the issue of air pollution with high ozone concentrations in the ambient air. The Aura ozone monitoring instrument (OMI), can provide long-term and large-scale dynamic monitoring of tropospheric column ozone (TCO). The TCO concentrations over China were extracted and analyzed from 2005 to 2019. The results showed that the spatiotemporal distribution of ozone concentration decreased from northeast to southeast in China. The seasonal variations of ozone concentration were spring’s concentration is greater than winter’s concentration, winter’s concentration is greater than summer’s concentration, summer’s concentration is greater than autumn’s concentration. The monthly variation showed a cyclical trend, with low values observed from June to November and high values from December to May. In addition, the spatiotemporal distribution of ozone concentration was affected by natural factors and anthropogenic factors. Regarding natural factors, TCO concentration was positively correlated with temperature, wind field, and vegetation coverage, but negatively with precipitation in southwest China. Regarding anthropogenic factors, TCO concentration showed a significant positive correlation with regional GDP, oxides of nitrogen (NOx), and volatile organic compounds (VOCs) in most areas of China. Clustering of backward air trajectories revealed that northwest and southeast airflows led to a higher TCO concentration in northeast China. These further indicate that the variation of TCO concentration was affected by many factors, but temperature, wind field, and the emissions of NOx and VOCs were the key factors.

Data fusion of atmospheric ozone remote sensing Lidar according to deep learning

This article aims to realize the combined application of satellite remote sensing data and Lidar data fusion of atmospheric ozone in the ozone distribution prediction in western China. Firstly, the ozone remote sensing data and Lidar vertical monitoring data detected by ozone monitoring instruments are collected from various provinces in western China; the recurrent neural network (RNN) model based on the gated recurrent unit and the convolutional neural network (CNN) model based on time series are constructed and integrated to obtain the RNN–CNN model for ozone concentration prediction, and the prediction performance is compared with that of the RNN and CNN model. Satellite remote sensing data show that the total ozone concentration in western China has obvious spatial distribution characteristics, and can be elevated with the increase in latitude; the ozone concentration in the north is higher and the ozone concentration in the south is lower in different seasons in western China, and it in autumn and summer is lower than that in spring and winter generally. The hour-by-hour change of ozone concentration shows that the ozone concentration in different seasons reaches a trough around 9–10 o’clock, and reaches a peak around 16–17 o’clock. When the RNN–CNN model is applied to predict the hourly change of ozone concentration within a day, it can achieve the best root-mean-squared error (18.13), mean absolute error (11.64), and index of agreement (0.973). The monitoring results of Lidar data at a certain site are compared and analyzed, and the results show that the fitness is the best between the predicted value of ozone concentration obtained by the RNN–CNN model and the actually measured value. This research provides the possibility for the intelligent prediction of atmospheric ozone concentration distribution and has reliable theoretical value and practical significance.

Intercomparison of Sonde, WRF/CAMx and Satellite Sounder Profile Data for the Paso Del Norte Region

The Paso Del Norte (PdN) region comprises the city of El Paso, TX, Ciudad Juarez, Mexico, and some neighboring cities in the state of New Mexico. Developing a regional weather model for this specific region has always been challenging due to its complex terrain. To obtain more accurate weather and pollution forecasting for the PdN region, the results of the downscaled WRF (Weather Research and Forecast) model were intercompared with meteorological satellite data, with ground and radiosonde dataset. In addition, it is critical to analyze the distributions of ozone concentrations to better understand atmospheric aerosol concentrations and predict them both more accurately. Hence, in this study the ozone results of CAMx (Comprehensive Air Quality Model with Extensions) were extensively intercompared with ozonesonde data. The radiosonde/ozonesonde data were obtained throughout a campaign conducted during the summer of 2017 in the PdN region. Different meteorological variables such as temperature, pressure, relative humidity, wind speed, and ozone concentrations were used for comparison at several locations in the PdN region. The TCEQ (Texas Commission of Environment Quality) data from different CAMS (Continuous Ambient Monitoring Stations) were used for ground data intercomparison with the WRF results. The meteorological satellite sounding data were retrieved using an in-house satellite antenna receiver. The results of this research paper will not only provide better pollution forecasting capability for the PdN region but also for other regions with similar topography and terrain.

A novel simulation method for predicting ozone generation in corona discharge region

In this paper, a novel ozone prediction model was developed based on the mechanism of ozone generation in the corona discharge region that ionized region would make oxygen become ozone. The coupled solving equation of electrostatic field and ozone concentration were established, which considered the influence factors of polarity, voltage, current, free diffusion, and gas velocity. A pin-cylinder corona discharge device was used to observe the status of corona discharge and measure the ozone concentration in positive and negative polarity conditions, respectively. Results showed that the corona discharge region in two polarity conditions presented the different status and gave rise to unlike ozone concentration distribution. The novel ozone prediction model in this work was confirmed as an accurate tool to predict the ozone generation and distribution in the fluid domain, and would exert its essential role in the guidance of ESPs design and the control of ozone risk.

Evaluation of ozone risk under different air-supply mode in aircraft cabins

PurposeThe purpose of this paper is to evaluate the ozone risk introduced by the mixing air-supply mode, displacement air-supply mode and personalized air-supply mode, respectively, in commercial aircraft cabins.Design/methodology/approachIn this study, a computational fluid dynamics (CFD) model of aircraft cabin has been built to study the distribution of ozone mass fraction and the ozone surface deposition rate on passenger’s face and clothes under the three different air-supply modes, respectively. The distribution of ozone mass fraction has been obtained by calculating the mass concentration of ozone in different location. The ozone surface deposition rate on passenger’s face and clothes has been calculated according to the mechanism of the reactions between ozone and squalene, which is the primary reactant in human sebum.FindingsBy comparing the three air-supply modes, it was considered that the mixing air-supply mode made lower ozone concentration and ozone surface deposition risk in most area, but this was because of the thin air distribution in cabin. The displacement air-supply mode made an uneven distribution of ozone concentration and increased absorbing ozone risk in the breathing zone. The personalized air-supply mode was proper for avoiding ozone harm and making a comfortable air environment. The air supply from the inlet on seat back could not increase the ozone surface deposition risk on passenger’s face.Originality/valueThis paper provides the qualitative and quantitative analysis for ozone risk to the passengers under the different air-supply modes. Findings can provide some suggestions for the designers to optimize the air-supply mode of air distribution system for reducing passengers’ discomfort caused by high-altitude ozone introduction, such as breathing in too much ozone.

Tropospheric ozone assessment report: Global ozone metrics for climate change, human health, and crop/ecosystem research.

Assessment of spatial and temporal variation in the impacts of ozone on human health, vegetation, and climate requires appropriate metrics. A key component of the Tropospheric Ozone Assessment Report (TOAR) is the consistent calculation of these metrics at thousands of monitoring sites globally. Investigating temporal trends in these metrics required that the same statistical methods be applied across these ozone monitoring sites. The nonparametric Mann-Kendall test (for significant trends) and the Theil-Sen estimator (for estimating the magnitude of trend) were selected to provide robust methods across all sites. This paper provides the scientific underpinnings necessary to better understand the implications of and rationale for selecting a specific TOAR metric for assessing spatial and temporal variation in ozone for a particular impact. The rationale and underlying research evidence that influence the derivation of specific metrics are given. The form of 25 metrics (4 for model-measurement comparison, 5 for characterization of ozone in the free troposphere, 11 for human health impacts, and 5 for vegetation impacts) are described. Finally, this study categorizes health and vegetation exposure metrics based on the extent to which they are determined only by the highest hourly ozone levels, or by a wider range of values. The magnitude of the metrics is influenced by both the distribution of hourly average ozone concentrations at a site location, and the extent to which a particular metric is determined by relatively low, moderate, and high hourly ozone levels. Hence, for the same ozone time series, changes in the distribution of ozone concentrations can result in different changes in the magnitude and direction of trends for different metrics. Thus, dissimilar conclusions about the effect of changes in the drivers of ozone variability (e.g., precursor emissions) on health and vegetation exposure can result from the selection of different metrics.

Validation and application of CPFD models in simulating hydrodynamics and reactions in riser reactor with Geldart A particles

A Computational Particle Fluid Dynamics (CPFD) model, which has the ability of revealing the coupled flow-reaction behaviors on the scales ranging from an individual particle to the whole reactor, was developed for studying the gas-solids circulating fluidized bed (CFB) riser reactor handling Geldart A particles. The model was validated against available experimental data with respects to the hydrodynamics (e.g. distributions of solids holdup and solids velocity) and the reaction behaviors (e.g. distribution of ozone concentration for ozone decomposition), and acceptable agreements were achieved between the modeling results and the experimental data. The CPFD model was further extended for analyzing the gas-solids flow hydrodynamics and the cracking reactions in an industrial Fluid Catalytic Cracking (FCC) riser reactor. Modeling results indicate that the volume of gas in the CFB riser will vary due to the presence of interphase chemical reactions (e.g. catalytic cracking reactions in FCC), which can significantly influence the gas-solids flow hydrodynamics. It was demonstrated that the CPFD model can be applied to quantify the relationship between the flow hydrodynamics and the chemical reactions in multiphase flow-reaction systems.

Hydraulic and disinfection efficiency of an ozonation contactor for a municipal water treatment plant using computational fluid dynamics

ABSTRACTDetermination of hydrodynamic characteristics and disinfection efficiency are important factors in the operational study of the ozone contactors. In the present work, a full‐scale ozonation contactor for a municipal water treatment plant was simulated by computational fluid dynamics (CFD) approach. The Eulerian multiphase, k–ϵ turbulence, and kinetic‐based reaction models were implemented in a finite volume framework to predict the hydraulic parameters, ozone concentration distribution, total organic carbon (TOC) reduction, and concentration‐contact time (CT) values in the contactor. The reaction kinetics for ozone decay was determined by experimental data collected via the contactor. The quality of mesh near the walls and the results independence on the mesh density were probed to determine the optimal grid density. For the first time, the accuracy of the model was evaluated in contrast with experimental data of TOC reductions during ozonation process. The pulse tracer study and flow field indicated recirculation, dead zones, and therefore non‐optimized baffle configuration in the contactor. The predicted CT values indicated that in the cold and hot months, the ozone dosage must increase by a factor of 3–5 relative to the current dosage values to achieve disinfection log‐inactivation credit for pathogens. Using the CFD simulation results, the correlation between ozone dosage and CT values for the hot and cold months in the ozonation contactor was predicted.

Distribution ozone concentration in Klang Valley using GIS approaches

Today, ozone has become one of the main air pollutants in Malaysia. The high ozone precursor concentrations have been encouraging the ozone production. The development of the Klang Valley, Malaysia has many types of physical activities such as urban commercial, industrial area, settlement area and others, which has increased the risk of atmospheric pollution. The purpose of this paper is to determine the spatial distribution between types of land use and ozone concentration that are occurred in the year 2014. The study areas for this paper include Shah Alam, Kajang, Petaling Jaya and Port Klang. Distribution of ozone concentration will be showed via spatial analysis tools in Geographic Information Systems (GIS) approached and the types of land use will be extracted using Remote Sensing technique. The result showed 97 ppb (parts-per-billion, 10-9) and 161 ppb recorded at Port Klang and Shah Alam respectively that are mainly represented by the settlement area. Therefore, the physical land use need to be monitor and controlled by the government in order to make sure the ozone production for daily per hour will not exceed the regulation allowed.

Responses of human health and vegetation exposure metrics to changes in ozone concentration distributions in the European Union, United States, and China

The impacts of surface ozone (O3) on human health and vegetation have prompted O3 precursor emission reductions in the European Union (EU) and United States (US). In contrast, until recently, emissions have increased in East Asia and most strongly in China. As emissions change, the distribution of hourly O3 concentrations also changes, as do the values of exposure metrics. The distribution changes can result in the exposure metric trend patterns changing in a similar direction as trends in emissions (e.g., metrics increase as emissions increase) or, in some cases, in opposite directions. This study, using data from 481 sites (276 in the EU, 196 in the US, and 9 in China), investigates the response of 14 human health and vegetation O3 exposure metrics to changes in hourly O3 concentration distributions over time. At a majority of EU and US sites, there was a reduction in the frequency of both relatively high and low hourly average O3 concentrations. In contrast, for some sites in mainland China and Hong Kong, the middle of the distribution shifted upwards but the low end did not change and for other sites, the entire distribution shifted upwards. The responses of the 14 metrics to these changes at the EU, US, and Chinese sites were varied, and dependent on (1) the extent to which the metric was determined by relatively high, moderate, and low concentrations and (2) the relative magnitude of the shifts occurring within the O3 concentration distribution. For example, the majority of the EU and US sites experienced decreasing trends in the magnitude of those metrics associated with higher concentrations. For the sites in China, all of the metrics either increased or had no trends. In contrast, there were a greater number of sites that had no trend for those metrics determined by a combination of moderate and high O3 concentrations. A result of our analyses is that trends in mean or median concentrations did not appear to be well associated with some exposure metrics applicable for assessing human health or vegetation effects. The identification of shifting patterns in the O3 distribution and the resulting changes in O3 exposure metrics across regions with large emission increases and decreases is an important step in examining the linkage between emissions and exposure metric trends. The results provide insight into the utility of using specific exposure metrics for assessing emission control strategies.