Spatial Distribution Of Ozone 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.

Light absorption enhancement of black carbon in a pyrocumulonimbus cloud

Pyrocumulonimbus (pyroCb) firestorm systems have been shown to inject significant amounts of black carbon (BC) to the stratosphere with a residence time of several months. Injected BC warms the local stratospheric air, consequently perturbing transport and hence spatial distributions of ozone and water vapor. A distinguishing feature of BC-containing particles residing within pyroCb smoke is their thick surface coatings made of condensed organic matter. When coated with non-refractory materials, BC’s absorption is enhanced, yet the absorption enhancement factor (Eabs) for pyroCb BC is not well constrained. Here, we perform particle-scale measurements of BC mass, morphology, and coating thickness from inside a pyroCb cloud and quantify Eabs using an established particle-resolved BC optics model. We find that the population-averaged Eabs for BC asymptotes to 2.0 with increasing coating thickness. This value denotes the upper limit of Eabs for thickly coated BC in the atmosphere. Our results provide observationally constrained parameterizations of BC absorption for improved radiative transfer calculations of pyroCb events.

A spatial analysis of ozone and PM2.5 distribution for assessing design factors of healthy buildings

Indoor air quality (IAQ) represents one of the significant challenges in healthy building design, with thermal comfort and daylighting being primary design objectives. This study aims to demonstrate the potential of energy-independent building infrastructure as a passive design solution to mitigate the impact of outdoor air pollution. This paper presents experimental and numerical investigations to establish the relationship between design factors (e.g., building orientation, geometry, and window opening location) and the spatial distribution of ozone and fine particulate matter (PM2.5) in residential houses. Our case study involves experimentally validated computational fluid dynamics (CFD) simulations for typical rowhouse archetypes in one of the United States' most polluted cities. Our findings indicate that higher ozone levels are observed on upper floors, posing a significant exposure risk at a breathing zone height of 1.7 m. In contrast, PM2.5 levels are most concerning at a height of 1 m, directly impacting children's health. Alarmingly, future trends of ozone pollution suggest that these risks could be almost three times higher compared to current conditions. Given the distinctive diurnal patterns of outdoor air pollution, the study also finds that the strategic placement of windows for nighttime air flushing, specifically in the upper two-thirds of the building, can improve IAQ. Our analysis of the spatial placement of the air purifier reveals that the optimal location is at the middle center of the windward wall. Our work underscores the potential of building design features in mitigating pollution exposure in indoor environments, emphasizing the importance of architectural solutions in public health.

Spatialtemporal evolution characteristics of ozone in China and its response to urbanization.

Based on the background of urbanization in China, we used the dynamic spatial panel Durbin model to study the driving mechanism of ozone pollution empirically. We also analyzed the spatial distribution of ozone driving factors using the GTWR. The results show that: i) The average annual increase of ozone concentration in ambient air in China from 2015 to 2019 was 1.68μg/m3, and 8.39μg/m3 elevated the year 2019 compared with 2015. ii) The Moran's I value of ozone in ambient air was 0.027 in 2015 and 0.209 in 2019, showing the spatial distribution characteristics of "east heavy and west light" and "south low and north high". iii) Per capita GDP industrial structure, population density, land expansion, and urbanization rate have significant spillover effects on ozone concentration, and the regional spillover effect is greater than the local effect. R&D intensity and education level have a significant negative impact on ozone concentration. iv) There is a decreasing trend in the inhibitory effect of educational attainment and R&D intensity on ozone concentration, and an increasing trend in the promotional effect of population urbanization rate, land expansion, and economic development on ozone concentration. Empirical results suggest a twofold policy meaning: i) to explore the causes behind the distribution of ozone from the new perspective of urbanization, and to further the atmospheric environmental protection system and ii) to eliminate the adverse impacts of ozone pollution on nature and harmonious social development.

The spatiotemporal variations of total column ozone concentration over Ethiopia

We have studied spatiotemporal characteristics of ozone concentration over Ethiopia using data from the Ozone Mapper and Profiling Suite-Nadir Mapper (OMPS-NM). Daily total column ozone data of 108 observation points with spatial resolution 1° × 1° over the study area for the period of 2012–2019 have been analyzed. The spatial variations over the region have been studied by considering longitudinal and latitudinal bands separately through the sample mean difference among different bands using multi-comparison analysis of variance technique in order to identify the clusters in the region. For the temporal variability, we model the total column ozone timeseries observation as a sum of seasonal, trend, and temporally correlated noise components. We have found that the total column ozone concentration has a maximum value of 301DU during summer on August 18, 2013 and a minimum value of 216DU during winter on January 03, 2013 over the study period. The 95% confidence level of the overall mean of the total column ozone concentration during the study period was found to be (261.35 ± 2.38)DU. Our spatial data analysis revealed that the spatial distribution of ozone over Ethiopia can be classified into three clusters: southern cluster (4.5°N–8.5°N and 33.5°E–47.5°E), north-eastern cluster (9.5°N to 14.5°N and 41.5°E–47.5°E), and north-western cluster (9.5°N–14.5°N and 33.5°E–40.5°E). We have checked the coefficient of determination among bands in the same cluster to see if the concentration of ozone in one band can be explained by the concentration in another band for each cluster and confirmed the reliability of the classification. In order to capture temporal characteristics, we have computed the spectral periodogram for each cluster and obtained a power peak at frequency f = 0.002 768 Hz, which indicates that the ozone concentration over the region exhibits an annual cyclic behavior. A truncated Fourier series fit is used to determine the annual seasonal component. The non-parametric Mann–Kendall’s trend test with a 95% confidence level of significant has indicated a decreasing linear trend with a depletion rate of 0.77, 0.73, and 0.43 DU/yr over north-western, north-eastern, and southern clusters, respectively. The analysis of residuals for each cluster indicated that the residuals are normally distributed with no significant outliers, and the model explains 85%, 86%, and 79% of the variance in the north-western, north-eastern, and southern clusters, respectively, demonstrating the reliability of the model considered in this study.

Temporal and Spatial Distribution of Ozone and Its Influencing Factors in China

Tropospheric ozone (O3) pollution has emerged as a significant concern, as it can adversely influence human health, daily activities, and the surrounding environment(The following tropospheric O3 is referred to as O3). Research on the societal contribution to O3 primarily concentrates on the generation mechanisms and chemical processes, with limited studies examining the influence of social and economic activities on O3 at a national scale. In this investigation, spatial econometric models, random forest models, and geographically weighted regression (GWR) were adopted for assessing the effects of meteorological, natural, and socioeconomic factors on O3 concentration throughout the country. The spatial error model (SEM) revealed that precipitation, temperature, wind direction, per capita GDP, RD project funding, and SO2 were the primary factors influencing O3 concentration in China, among which precipitation had the strongest effect on O3, followed by temperature and SO2. Subsequently, the GWR model was utilized to demonstrate the regional differences in the impacts of precipitation, NOx, secondary industry proportion, and electricity consumption. In central and western regions, such as Jiangxi, Guangxi, and Guizhou, precipitation, NOx, and power consumption were the leading factors contributing to severe O3 pollution. The secondary industry proportion substantially affected O3 pollution in the Beijing-Tianjin-Hebei region, indicating that this sector played a crucial role in the region’s economic growth and contributed to elevated O3 concentrations. Meteorological, natural, and socioeconomic factors exhibited a lesser influence on O3 pollution in most eastern regions compared to central and western regions. This study’s findings identified the primary contributors to O3 pollution and provided a scientific basis for developing strategies to mitigate its impact.

Development of mobile lidar for remote sensing of tropospheric ozone on the vertical and horizontal (slant) paths

We present a mobile lidar for measuring, in the ultraviolet (UV) region of the spectrum, the spatial distribution of ozone (O3) in the troposphere on the vertical and horizontal (slant) sensing paths, using the four-channel system of recording the return signals. The lidar operates, based on the lidar differential absorption and scattering method on the wavelength pair of 299 nm and 341 nm. We present a technical description of the lidar developed.

Correlation between extreme/ unusual ozone events and corona effect from electric power transmission & distribution grids. Assessing the health and environmental impact of high-voltage direct current (HVDC) grid on ozone ground levels in Sao Paulo metropolitan area, Brazil.

• BACKGROUND AND AIM 1- Ozone ground levels pose a risk for public health 2- The increasing ozone levels measured in parts of the metropolitan area of Sao Paulo have brought the attention of researchers. Proposed mechanisms do not provide a clear outcome for establishing root causes, and for clarifying sources of ozone. Spatial distribution of ozone also depends on wind patterns. 3- As an alternate hypothesis for explaining unusual high levels of ozone, the corona discharge from electric transmission grid, known as corona effect, can play a significative role in the absence of, or in coincidence with other concomitant factors, to explain ozone (O3) and nitric oxide (NO) unusual levels. Negative coronas emits more ozone than Positive coronas. 4- The Environmental Protection Agency- EPA, found in a report of 1973 (EPA-650/4-73-003), following a research carried out in areas of high concentration of transmission lines that "under minimal wind conditions, such transmission line concentrations can produce sizeable local ozone levels.” 5- Research applies geospatial and statistical analysis with the aim of better understanding correlations between exposures and ozone levels. • METHODS Geographic Information System (GIS)-based systems with average grid cell edge length of 10 km minimum. Global InMAP (Intervention Model for Air Pollution) with a variable resolution grid (4 km horizontal grid cell widths) in combination with GEOS- Chem simulations. Data analysis by machine-learning model. • RESULTS Applying Predictive Machine Learning algorithm as regression models, research found that coronas could generate significant ozone levels downwind of the power line. • CONCLUSIONS Evidence of correlation between unusual ozone measurements and location of key power electric grid transmission lines and distribution centers with potential for corona effect are found in Sao Paulo state and Sao Paulo city metropolitan area. • KEYWORDS Corona effect, ozone, electric grids, spatial simulation, wind patterns, air pollution

Analysis of atmospheric ozone in Fenwei Plain based on remote sensing monitoring.

This study uses the daily product data of the concentration of ozone in the atmospheric column (ozone column concentration) collected by the Aura satellite's Ozone Monitoring Instrument (OMI), to evaluate the ozone pollution status of the Fenwei Plain in east-central China, by employing pixel-based spatial analysis, an θslope trend index, a Hurst index, and grey correlation. The following results were found. (1) The spatial distribution of ozone in the atmosphere of the Fenwei Plain was higher in the north and lower in the south, with high values appearing in Jinzhong, Lvliang, and other cities. (2) The changes in ozone column concentration periodically and seasonally in the Fenwei Plain. Seasonally, the ozone column concentration was highest in spring, followed by summer, winter, and autumn. (3) The pixel-based trend change of the ozone slope (θslope) indicated that the ozone concentration in the region was in a downward trend, while the long-term correlation of the time series trend Hurst index found that the region should expect to see a weak rebound in the ozone column concentration in the future, so that routine monitoring should be strengthened. (4) The present study on the factors influencing the ozone concentration found that the concentration is relatively closely related to temperature, air pressure, humidity, grain sowing area, highway mileage, and secondary industry.

Prediction and evaluation of spatial distributions of ozone and urban heat island using a machine learning modified land use regression method

• Synergistic curb of ozone and urban heat island. • LUR modified by machine learning has better performance. • There is spatial interaction between ozone and urban heat island. • Scientific increase of green space is conducive to curb ozone and UHI. In summer, Ozone (O 3 ) pollution and urban heat island (UHI) pose serious health risks to humans. To obtain the spatial distributions of ozone and urban heat island in Xi'an in summer and develop a simultaneous control strategy of ozone and urban heat island, the land use regression model is modified and improved using the machine learning random forest algorithm. The LUR-Kriging-RF integrated prediction model is then established. The land use regression and kriging are used to extract the feature variables, while random forest is used to establish a regression model. The spatial distribution maps of ozone and urban heat island in Xi'an are obtained by regression mapping of the prediction model, and the spatial relationships between them are analyzed. The SHapley Additive explanation (SHAP) and partial dependence plot (PDP) are adopted to explain the way feature variables act on ozone and urban heat island. Based on the spatial distribution and interaction mode, a simultaneous control strategy of ozone and urban heat island in Xi'an is put forward. For ozone, the R 2 of the integrated prediction model (0.65) is higher than that of land use regression (0.4), while the RMSE (28.18) of the integrated model is lower than that of land use regression (35.66). For temperature, the R 2 of the integrated model (0.93) is higher than that of land use regression (0.8), while its RMSE (0.92) is lower than that of land use regression (1.52). The performance of the LUR-Kriging-RF integrated prediction model is better than that of land use regression. This study reveals the spatial interactions between ozone and urban heat island in the central urban areas. The suitable strategies for mapping ozone pollution and urban heat island control include reducing VOCs emissions from industrial sources and agricultural sources, increasing plants with low VOCs emissions, and spray humidification. This study can be used to evaluate ozone exposure and thermal exposure, provide scientific support for environmental protection and urban heat island control policies, contribute to reducing public health threats, promote the sustainability of urban environments, and promote the practical application of machine learning in this field.

Spatio‐Temporal Hourly and Daily Ozone Forecasting in China Using a Hybrid Machine Learning Model: Autoencoder and Generative Adversarial Networks

AbstractEfficient and accurate real‐time forecasting of national spatial ozone distribution is critical to the provision of effective early warning. Traditional numerical air quality models require a high computational cost associated with running large‐scale numerical simulations. In this work, we introduce a hybrid model (VAE‐GAN) combining a generative adversarial network (GAN) with a variational autoencoder (VAE) to learn the dynamic ozone distributions in spatial and temporal spaces. The VAE‐GAN model can not only decipher the complex nonlinear relationship between the inputs (the past states/ozone and meteorological factors) and outputs (ozone), but also provide ozone forecasts for a long lead‐time beyond the training period. The performance of VAE‐GAN is demonstrated in hourly and daily spatio‐temporal ozone forecasts over China. The training datasets from 2013 to 2017 and validation datasets from 2018 to 2019 are the collection of data from the air quality reanalysis datasets. With the use of VAE, large dataset sizes are decreased by three orders of magnitude, enabling hourly and daily forecasts to be computed in seconds. Results show that the VAE‐GAN achieves a reasonable accuracy in the prediction of both the spatial and temporal evolution patterns of hourly and daily ozone fields, as compared to the Nested Air Quality Prediction Modeling System (commonly used in China), the reanalysis data and observations during the validation period. Thus, the VAE‐GAN is a cost‐effective tool for large data‐driven predictions, which can potentially reinforce air pollution prediction efforts in providing risk assessment and management in a timely manner.

Spatial patterns in summertime surface ozone in the Southern Front Range of the U.S. Rocky Mountains

Summertime ozone in the Western United States presents a unique public health challenge. Changes in population, background ozone, wildland fire, and local precursor emissions combined with terrain-induced meteorology can affect surface ozone levels and compliance with the National Ambient Air Quality Standards (NAAQS). While there is considerable research on ozone in the Northern Front Range Metropolitan Area of Colorado, United States, less is known about the Southern Front Range. In Colorado Springs, approximately 100 km south of Denver, summertime maximum daily 8-h average (MDA8) ozone shows no significant (p < .05) trend at the 5th, 50th, or 95th percentile over the past 20 years. However, the region is at risk of nonattainment with the NAAQS based on observations from 2018 to 2020. From June through September 2018, the Colorado Department of Public Health and Environment measured hourly ozone at eight sites to characterize the spatial distribution of ozone in Colorado Springs. Mean ozone (±1σ) ranged from 34 ± 19 to 60 ± 9 ppb. The 95th percentile of hourly ozone increased approximately 1.1 ppb per 100 m of elevation, while the amplitudes of mean diurnal profiles decreased with elevation and distance from the interstate. MDA8 ozone was also highly correlated across all sites, and there is little evidence of local photochemical production or ozone transport from Denver. Further, results from generalized additive modeling show that summertime MDA8 in this region is strongly influenced by regional background air and wildfire, with smoke contributing an average of 4–5 ppb to the MDA8. Enhanced MDA8 values due to wildfires were especially pronounced in 2018 and 2020. Lastly, we find that the permanent monitoring sites represent the lower end of observed ozone in the region, suggesting that additional long-term monitoring for public health may be warranted in populated, higher elevation areas.

Statistical Analysis of the Spatiotemporal Distribution of Ozone Induced by Cut-Off Lows in the Upper Troposphere and Lower Stratosphere over Northeast Asia

This paper presents the results of a statistical study of the spatiotemporal distribution of ozone in the upper troposphere and lower stratosphere (UTLS) regions induced by cut-off lows (COLs) over Northeast Asia. The analysis was based on high-resolution ERA-Interim ozone data and Atmospheric Infrared Sounder (AIRS) satellite data for the period from 2005–2015. A total of 186 COL events were detected. The observed ozone distribution revealed an ozone-rich region in the upper troposphere (300 hPa) located around the center of the COLs at the time when COLs reached their maximum intensity. This region corresponds to a region of high potential vorticity (PV). In the middle troposphere (500 hPa), enhanced levels of the ozone were distributed in two regions. The maximum concentration was located to the east of the COLs, and a secondary maximum region was in the center of the COLs. Further analysis revealed that this spatial distribution of ozone in the upper troposphere was affected mainly by decreased tropopause. The ozone was subject to a ‘rotary’ transport process in the middle troposphere, influenced mainly by the anticlockwise circulation of the COLs and the surrounding horizontal wind distribution. The temporal variations in ozone anomalies also revealed the ozone distribution patterns and transport processes. The variation in ozone anomalies implied that the magnitude of the ozone increase was closely related to the evolution of COLs lifecycle. The temporal and spatial distributions of the ozone revealed by the statistical analysis of the AIRS satellite data were overall consistent with those of the ERA-Interim data.

Temporal and Spatial Ozone Distribution over Egypt

The long-term temporal trends and spatial distribution of Ozone (O3) over Egypt is presented using monthly data from both the Atmospheric Infrared Sounder (AIRS) and the model Modern-Era Retrospective analysis for Research and Applications (MERRA) datasets. The twelve-year monthly record (2005–2016) of the Total Ozone Column (TOC) has a spatial resolution of 1 × 1° from AIRS and 0.5 × 0.625° from the MERRA-2 dataset. The average monthly, seasonal and interannual time series are analyzed for their temporal trends, while the seasonal average spatial distributions are compared. It was found that MERRA-2 underestimated AIRS measurements. Both AIRS and MERRA-2 have their minimum monthly averages of TOC in February 2013. The maximum monthly average TOC from AIRS is 321.48 DU in July 2012, while that from MERRA-2 is 303.48 in April 2011.

Spatial distribution of ozone over Indonesia (Study case: Forest fire event 2015)

Tropospheric ozone is known as surface ozone and caused several health impact. The objective of this study was to analysis spatial distribution of tropospheric ozone over Indonesia case study forest fire event in 2015. Monthly observation measured by Ozone Monitoring Instrument (OMI) have been analysed from January – December 2015 to study spatial distribution of tropospheric ozone related to forest fire event 2015. The study discovered high level of tropospheric column ozone (TCO) from October to November 2015. The result shows increasing average of TCO from September to October almost 6 DU. Meanwhile, monthly number of hotspot is higher in September 2015 with total number 257 hotspot which is acquired by Moderate Resolution Imaging Spectrometer (MODIS) Terra version 6.1 with confidence level same or more than 90%. The hotspot distribution compared with spatial TCO distribution and shows interesting time lag with respect to hotspot distribution, one month. Further study for daily comparison of TCO and forest fire event needed. This result suggested that the tropospheric ozone over the Indonesian region increases in 2015 were remarkable and corresponded to forest fire event.

Impact of East Asian Summer Monsoon on Surface Ozone Pattern in China

AbstractTropospheric ozone plays a key role in regional and global atmospheric and climate systems. In East Asia, ozone can be affected both in concentration level and spatial pattern by typical monsoon climate. This paper uses three different indices to identify the strength of East Asian summer monsoon (EASM) and explores the possible impact of EASM intensity on the ozone pattern through synthetic and process analysis. The difference in ozone between three strong and three weak monsoon years was analyzed using the simulations from regional climate model RegCM4‐Chem. It was found that EASM intensity can significantly influence the spatial distribution of ozone in the lower troposphere. When EASM is strong, ozone in the eastern part of China (28°N ‐ 42° N) is reduced, but the inverse is detected in the north and south. The surface ozone difference ranges from −7 to 7 ppbv during the 3 months (June to August) of the EASM, with the most obvious difference in August. Difference of the 3 months' average ozone ranges from −3.5 to 4 ppbv. Process analysis shows that the uppermost factor controlling ozone level during summer monsoon seasons is the chemistry process. Interannual variability of EASM can impact the spatial distribution of ozone through wind in the lower troposphere, cloud cover, and downward shortwave radiation, which affect the transport and chemical formation of ozone. The phenomenon should be addressed when considering the interaction between ozone and the climate in East Asia region.

A Comparative Study of Intra-Seasonal Variability of Total Column Ozone Measured over the Tropical Maritime and Coastal Stations Using Microtop II Ozonometer

The study of temporal and spatial distribution of ozone is very important for understanding the atmospheric chemistry and thereby its impact on environment, weather and climate. The intra-seasonal variability plays a major role in the inter-annual variability of weather parameters such as rainfall, temperature, pressure and atmospheric trace gas constituents such as atmospheric ozone. The strength of monsoon circulation and deep convection greatly modifies the atmospheric compositions and meteorological parameters such as rainfall amount, distributions of atmospheric trace gas concentrations and other weather parameters over the summer monsoon region. The daily total column ozone (TCO) measured over maritime station (Lakshadweep Island— 10°10'N & 73°30'E) and coastal station (Cochin—9°55'N and 76°16'E) using Microtop II Ozonometer were considered for the comparative study of seasonal and intra-seasonal variability for the year 2015. The annual average of total column ozone over Lakshadweep Islands and Cochin was 290 DU and 280 DU respectively for the year 2015. The greater concentrations in daily TCO measurements over Lakshadweep Islands for all seasons compared to Cochin lead to the speculations that, the surface ozone concentration is more because of pollution from the diesel burning emissions, since the whole Island’s population completely depends on diesel generator for the power supply. During winter season maritime station shows a decrease of ~30 DU in TCO over Lak-shadweep Island compared to coastal station Cochin (~18 DU) from the annual mean in the month of December. During pre-monsoon season TCO concentration is high over both locations. There is gradual increase of TCO concentration over Cochin from pre-monsoon to monsoon season and peak in the month of September, but decreasing TCO concentrations measured over Lakshadweep during July to August. In the analysis it was found that Intra-Seasonal Variability (ISV) in total column ozone over Lakshadweep Islands and Cochin during summer monsoon season was modulated by the monsoon dynamics and convection, thereby changes in the photochemistry of ozone production and distributions over the monsoon region. Two significant intra-seasonal oscillations (ISOs) such as Madden Julian Oscillation (MJO) and Quasi-Biweekly Oscillations (QBW) were identified in the TCO during monsoon season. The MJO shows higher periodicity (~54 days) over Lakshadweep Islands compared to the coastal station, Cochin (~48 days). Intra-seasonal variability of TCO over the maritime and coastal stations varies with geographic locations, marine boundary layer characteristics and also with seasons. The intra-seasonal variability or ISOs controls the interannual variability of TCO over a region. Hence deeper knowledge of ISOs in trace gases such as ozone helps us to understand more about the regional climate and air quality.

A reanalysis of ozone on Mars from assimilation of SPICAM observations

We have assimilated for the first time SPICAM retrievals of total ozone into a Martian global circulation model to provide a global reanalysis of the ozone cycle. Disagreement in total ozone between model prediction and assimilation is observed between 45°S–10°S from LS=135–180° and at northern polar (60°N–90°N) latitudes during northern fall (LS=150–195°). Large percentage differences in total ozone at northern fall polar latitudes identified through the assimilation process are linked with excessive northward transport of water vapour west of Tharsis and over Arabia Terra. Modelling biases in water vapour can also explain the underestimation of total ozone between 45°S–10°S from LS=135–180°. Heterogeneous uptake of odd hydrogen radicals are unable to explain the outstanding underestimation of northern polar total ozone in late northern fall.Assimilation of total ozone retrievals results in alterations of the modelled spatial distribution of ozone in the southern polar winter high altitude ozone layer. This illustrates the potential use of assimilation methods in constraining total ozone where SPICAM cannot observe, in a region where total ozone is especially important for potential investigations of the polar dynamics.

Impact of Taihu Lake on city ozone in the Yangtze River Delta

The lake-breeze at Taihu Lake generates a different specific heat capacity between the water body and the surrounding land. Taihu Lake has a significant impact on the atmospheric conditions and the air quality in the Yangtze River Delta. This phenomenon is referred to as the Taihu Lake effect. In this study, two simulations were conducted to determine the impact of the Taihu Lake effect in the reference experiment (R-E) and sensitivity experiments (NO_TH). The control simulations demonstrated that the meteorological field and the spatial distribution of ozone (O3) concentrations over Taihu lake obviously changed once the land-use type of water body was substituted by cropland. The surface temperature of Taihu Lake was reduced under the impact of Taihu Lake, and a huge temperature difference caused a strong lake-breeze effect. The results also showed that the difference in the average concentrations of O3 between the R-E and NO TH experiments reached 12 ppbv in most areas of Taihu Lake, all day, on 20 May 2014. During daytime (0800–1600 LST, LST=UTC+8), the influence of the Taihu Lake effect on O3 in the Suzhou region was not significant. However, the influence of the Taihu Lake effect on O3 in the Suzhou region was obvious during nighttime (1800–2400 LST). The larger changes in the physical and chemical processes were horizontal and vertical advections under the influence of the Taihu Lake effect in Taihu Lake.

Modelling ambient ozone in an urban area using an objective model and geostatistical algorithms

Ground-level tropospheric ozone is one of the air pollutants of most concern. Ozone levels continue to exceed both target values and the long-term objectives established in EU legislation to protect human health and prevent damage to ecosystems, agricultural crops and materials. Researchers or decision-makers frequently need information about atmospheric pollution patterns in urbanized areas. The preparation of this type of information is a complex task, due to the influence of several factors and their variability over time.In this work, some results of urban ozone distribution patterns in the city of Badajoz, which is the largest (140,000 inhabitants) and most industrialized city in Extremadura region (southwest Spain) are shown. Twelve sampling campaigns, one per month, were carried out to measure ambient air ozone concentrations, during periods that were selected according to favourable conditions to ozone production, using an automatic portable analyzer.Later, to evaluate the overall ozone level at each sampling location during the time interval considered, the measured ozone data were analysed using a new methodology based on the formulation of the Rasch model. As a result, a measure of overall ozone level which consolidates the monthly ground-level ozone measurements was obtained, getting moreover information about the influence on the overall ozone level of each monthly ozone measure. Finally, overall ozone level at locations where no measurements were available was estimated with geostatistical techniques and hazard assessment maps based on the spatial distribution of ozone were also generated.