Influences of cloud photochemical processes on tropospheric ozone

Rising frequency of ozone-favorable synoptic weather patterns contributes to 2015-2022 ozone increase in Guangzhou

Objective weather classification methods have been extensively applied to identify dominant ozone-favorable synoptic weather patterns (SWPs), however, the consistency of different classification methods is rarely examined. In this study, we apply two widely-used objective methods, the self-organizing map (SOM) and K-means clustering analysis, to derive ozone-favorable SWPs at four Chinese megacities in 2015-2022. We find that the two algorithms are largely consistent in recognizing dominant ozone-favorable SWPs for four Chinese megacities. In the case of classifying six SWPs, the derived circulation fields are highly similar with a spatial correlation of 0.99 between the two methods, and the difference in the mean frequency of each SWP is less than 7%. The six dominant ozone-favorable SWPs in Guangzhou are all characterized by anomaly higher radiation and temperature, lower cloud cover, relative humidity, and wind speed, and stronger subsidence compared to climatology mean. We find that during 2015-2022, the occurrence of ozone-favorable SWPs days increases significantly at a rate of 3.2 day/year, faster than the increases in the ozone exceedance days (3.0 day/year). The interannual variability between the occurrence of ozone-favorable SWPs and ozone exceedance days are generally consistent with a temporal correlation coefficient of 0.6. In particular, the significant increase in ozone-favorable SWPs in 2022, especially the Subtropical High type which typically occurs in September, is consistent with a long-lasting ozone pollution episode in Guangzhou during September 2022. Our results thus reveal that enhanced frequency of ozone-favorable SWPs plays an important role in the observed 2015-2022 ozone increase in Guangzhou.

Implications of ozone transport on air quality in the Sichuan Basin, China

Ozone pollution is formed through complex chemical and physical processes closely associated with emissions, photochemical reactions, and meteorological conditions. The objective of this study is to quantify the contributions of meteorological chemical formation, vertical transport, and horizontal transport to air quality during spring and summer in different regions of the Sichuan Basin. The Community Multi-scale Air Quality (CMAQ) with the Integrated Process Rate (IPR) was employed to simulate the months of April and July 2021 in the Sichuan Basin. The results indicate that both the spring and summer chemical formation of ozone in the urban centre show negative values, while the surrounding urban areas contribute positively, with chemical formation ranging from 0 to 10 μg·m-3. The maximum ozone level due to horizontal transport in the urban centre exceeds 20 μg·m-3, whereas horizontal transport in the surrounding urban areas exhibits negative values, with transport contributions concentrated within the range of -5 to 0 μg·m-3. The vertical transport in the central and southern parts of the basin shows positive values, with transport contributions ranging from 0 to 10 μg·m-3, and the urban centre exhibits relatively stronger vertical transport with contributions ranging from 10 to 20 μg·m-3. Although the chemical formation contribution in the urban centre is relatively small due to high nitrogen oxide emissions, vertical and horizontal transport play significant roles and are among the key factors contributing to ozone pollution formation.

Application of machine learning to analyze ozone sensitivity to influencing factors: A case study in Nanjing, China

Ground-level ozone (O3) has been an emerging concern in China. Due to its complicated formation mechanisms, understanding the effects of influencing factors is critical for making effective efforts on the pollution control. This study aims to present and demonstrate the practicality of a data-driven technique that applies a machine learning (ML) model coupled with the SHapley Additive exPlanations (SHAP) approach in O3 simulation and sensitivity analysis. Based on hourly measured concentrations of O3 and its major precursors, as well as meteorological factors in a northern area of Nanjing, China, a Light Gradient Boosting Machine (LightGBM) model was established to simulate O3 concentrations in different seasons, and the SHAP approach was applied to conduct in-depth analysis on the impacts of influencing factors on O3 formation. The results indicated a reliable performance of the ML model in simulating O3 concentrations, with the coefficient of determination (R2) between the measured and simulated larger than 0.80, and the impacts of influencing factors were reasonably evaluated by the SHAP approach on both seasonal and diurnal time scales. It was found that although volatile organic compounds (VOCs) and nitrogen oxides (NOx), as well as temperature and relative humidity, were generally the main influencing factors, their sensitivities to O3 formation varied significantly in different seasons and with time of the day. This study suggests that the data-driven ML model is a practicable technique and may act as an alternative way to perform mechanism analysis to some extent, and has immense potential to be applied in both problem research and decision-making for air pollution control.

Insights into the Formation Mechanism of Reactive Oxygen Species in the Interface Reaction of SO2 on Hematite

The interplay between sulfur and iron holds significant importance in their atmospheric cycle, yet a complete understanding of their coupling mechanism remains elusive. This investigation delves comprehensively into the evolution of reactive oxygen species (ROS) during the interfacial reactions involving sulfur dioxide (SO2) and iron oxides under varying relative humidity conditions. Notably, the direct activation of water by iron oxide was observed to generate a surface hydroxyl radical (•OH). In comparison, the aging of SO2 was found to markedly augment the production of •OH radicals on the surface of α-Fe2O3 under humid conditions. This augmentation was ascribed to the generation of superoxide radicals (•O2-) stemming from the activation of O2 through the Fe(II)/Fe(III) cycle and its combination with the H+ ion to produce hydrogen peroxide (H2O2) on the acidic surface. Moreover, the identification of moderate relative humidity as a pivotal factor in sustaining the surface acidity of iron oxide during SO2 aging underscores its crucial role in the coupling of iron dissolution, ROS production, and SO2 oxidation. Consequently, the interfacial reactions between SO2 and iron oxides under humid conditions are elucidated as atmospheric processes that enhance oxidation capacity rather than deplete ROS. These revelations offer novel insights into the mechanisms underlying •OH radical generation and oxidative potential within atmospheric interfacial chemistry.

Combining physical mechanisms and deep learning models for hourly surface ozone retrieval in China

As surface ozone (O3) gains increasing attention, there is an urgent need for high temporal resolution and accurate O3 monitoring. By taking advantage of the progress in artificial intelligence, deep learning models have been applied to satellite based O3 retrieval. However, the underlying physical mechanisms that influence surface O3 into model construction have rarely been considered. To overcome this issue, we considered the physical mechanisms influencing surface O3 and used them to select relevant variable features for developing a novel deep learning model. We used a wide and deep model architecture to account for linear and non-linear relationships between the variables and surface O3. Using the developed model, we performed hourly inversions of surface O3 retrieval over China from 2017 to 2019 (9:00-17:00, local time). The validation results based on sample-based (site-based) methods yielded an R2 of 0.94 (0.86) and an RMSE of 12.79 (19.13) μg/m3, indicating the accuracy of the models. The average surface O3 concentrations in China in 2017, 2018, and 2019 were 82, 78, and 87 μg/m3, respectively. There was a diurnal pattern in surface O3 in China, with levels rising significantly from 55 μg/m3 at 9:00 a.m. to 96 μg/m3 at 15:00. Between 15:00 and 16:00, the O3 concentration remained stable at 95 μg/m3 and decreased slightly thereafter (16:00-17:00). The results of this study contribute to a deeper understanding of the physical mechanisms of ozone and facilitate further studies on ozone monitoring, thereby enhancing our understanding of the spatiotemporal characteristics of ozone.

Influence of circulation types on temporal and spatial variations of ozone in Beijing

This study analyzes the impact of circulation types (CTs) on ozone (O₃) pollution in Beijing. The easterly high-pressure (SWW) circulation occurred most frequently (30%; 276 day), followed by northwesterly high-pressure (AN) circulation (24.3%; 224 day). The SWW type had the highest O₃ anomaly of +17.28 µg/m³, which was caused by excellent photochemical reactions, poor diffusion ability and regional transport. Due to the higher humidity and precipitation in the low-pressure type (C), the O₃ increase (+8.02 µg/m³) was less than that in the SWW type. Good diffusion/wet deposition and weak formation ability contributed to O₃ decrease in AN (-12.54 µg/m³) and northerly high-pressure (ESN) CTs (-12.26 µg/m³). The intra-area transport of O₃ was significant in polluted circulations (SWW- and C-CTs). In addition, higher temperature, radiation and less rainfall also contributed to higher O₃ in northern Beijing under the SWW type. For the clean CTs (AN and ESN CTs), precursor amount and intra-area transport played a dominant role in O₃ distribution. Under the northeasterly low-pressure CT, better formation conditions and higher precursor amount combined with the intra-area southerly transport to cause higher O₃ values in the south than in the north. The higher O₃ in the northwestern area under the northeasterly high-pressure type was influenced by weaker titration loss and high O₃ concentration in previous day. Annual variation in the CTs contributed up to 86.1% of the annual variation in O₃. About 78%-83% of the diurnal variation in O₃ resulted from local meteorological factors.

Rural vehicle emission as an important driver for the variations of summertime tropospheric ozone in the Beijing-Tianjin-Hebei region during 2014-2019

Tropospheric ozone (O₃) pollution is increasing in the Beijing-Tianjin-Hebei (BTH) region despite a significant decline in atmospheric fine aerosol particles (PM_2.5) in recent years. However, the intrinsic reason for the elevation of the regional O₃ is still unclear. In this study, we analyzed the spatio-temporal variations of tropospheric O₃ and relevant pollutants (PM_2.5, NO₂, and CO) in the BTH region based on monitoring data from the China Ministry of Ecology and Environment during the period of 2014-2019. The results showed that summertime O₃ concentrations were constant in Beijing (BJ, 0.06 µg/(m³•year)) but increased significantly in Tianjin (TJ, 9.09 µg/(m³•year)) and Hebei (HB, 6.06 µg/(m³•year)). Distinct O₃ trends between Beijing and other cities in BTH could not be attributed to the significant decrease in PM_2.5 (from -5.08 to -6.32 µg/(m³•year)) and CO (from -0.053 to -0.090 mg/(m³•year)) because their decreasing rates were approximately the same in all the cities. The relatively stable O₃ concentrations during the investigating period in BJ may be attributed to a faster decreasing rate of NO₂ (BJ: -2.55 µg/(m³•year); TJ: -1.16 µg/(m³•year); HB: -1.34 µg/(m³•year)), indicating that the continued reduction of NO_x will be an effective mitigation strategy for reducing regional O₃ pollution. Significant positive correlations were found between daily maximum 8 hr average (MDA8) O₃ concentrations and vehicle population and highway freight transportation in HB. Therefore, we speculate that the increase in rural NO_x emissions due to the increase in vehicle emissions in the vast rural areas around HB greatly accelerates regional O₃ formation, accounting for the significant increasing trends of O₃ in HB.

Temporal variation analysis, impact of COVID-19 on air pollutant concentrations, and forecasting of air pollutants over the cities of Bangalore and Delhi in India

Indian cities are highly vulnerable to atmospheric pollution in recent years, due to exponential growth in urbanisation and industrialisation, and the increased pollution has been made to focus on the temporal variation analysis and forecasting of air pollutants over major Indian cities like Delhi and Bangalore. PM_2.5 concentrations are nearly 60.5% less than the annual average value during monsoon season while 76.3% more during the winter months. Ozone concentrations increase during the summer months (~ 46.3% more than the annual average) in Delhi, whereas in Bangalore, ozone concentrations are more (~ 75% more than the annual average) during the winter months. Variations of carbon monoxide and nitrogen oxides are significantly less comparatively. COVID-19 lockdown has a substantial positive impact on air pollution. Air pollutant concentrations are reduced during phase I and phase II of the lockdown. Pollutants, especially NOx and PM_2.5 concentrations, are drastically reduced compared to the previous years. NOx concentrations are reduced by ~ 20% in Bangalore, whereas ~ 50% in Delhi. PM_2.5 concentrations are reduced by ~ 41% in Delhi and ~ 55% in Bangalore. Forecasting of pollutants will be helpful in providing the valuable information for the optimal air pollution control strategies. It has been observed that linear model gives better results compared to ARIMA and Exponential Smoothening models. By forecasting, the concentration of NO₂ is 115.288 µg/m³, the ozone is 30.636 µg/m³, SO₂ is 11.798 µg/m³, and CO is 2.758 mg/m³ over Delhi in 2021. All the pollutants during forecasting showed a rising trend except sulphur dioxide.

Diverse response of surface ozone to COVID-19 lockdown in China

Ozone (O3) is a key oxidant and pollutant in the lower atmosphere. Significant increases in surface O3 have been reported in many cities during the COVID-19 lockdown. Here we conduct comprehensive observation and modeling analyses of surface O3 across China for periods before and during the lockdown. We find that daytime O3 decreased in the subtropical south, in contrast to increases in most other regions. Meteorological changes and emission reductions both contributed to the O3 changes, with a larger impact from the former especially in central China. The plunge in nitrogen oxide (NOx) emission contributed to O3 increases in populated regions, whereas the reduction in volatile organic compounds (VOC) contributed to O3 decreases across the country. Due to a decreasing level of NOx saturation from north to south, the emission reduction in NOx (46%) and VOC (32%) contributed to net O3 increases in north China; the opposite effects of NOx decrease (49%) and VOC decrease (24%) balanced out in central China, whereas the comparable decreases (45-55%) in these two precursors contributed to net O3 declines in south China. Our study highlights the complex dependence of O3 on its precursors and the importance of meteorology in the short-term O3 variability.

Low particulate nitrate in the residual layer in autumn over the North China Plain

High ozone concentrations promote the formation of nitrate in the nocturnal residual layer (RL), but this phenomenon has not been confirmed by direct observation. In this study, ozone, water-soluble ions in PM_2.5 and the corresponding meteorological factors in the stable boundary layer, RL and mixing layer were observed by portable instruments carried on a tethered balloon over the North China Plain. The ozone concentration significantly increased in the RL compared to that in the stable boundary layer, while particulate nitrate significantly decreased, except in the clouds. Unfavorable environmental conditions, i.e., high temperature, low relative humidity, low aerosol surface area, and weak particle acidity, are not conducive to dinitrogen pentoxide uptake and hydrolysis to form particulate nitrate in the RL, and are conducive to the volatilization of nitrate to a gaseous state. Thus, our observations differed from traditional reports and confirmed that the morning peak of particulate nitrate at ground level is not related to the downward transport of nitrate from the RL. In addition, evidence for nitrate formation in cloudy weather is provided, and the possible impact on ozone is discussed.

Prediction of tropospheric ozone concentration using artificial neural networks at traffic and background urban locations in Novi Sad, Serbia

In this paper, we described generation and performances of feedforward neural network models that could be used for a day ahead predictions of the daily maximum 1-h ozone concentration (1hO₃) and 8-h average ozone concentration (8hO₃) at one traffic and one background station in the urban area of Novi Sad, Serbia. The six meteorological variables for the day preceding the forecast and forecast day, ozone concentrations in the day preceding the forecast, the number of the day of the year, and the number of the weekday for which ozone prediction was performed were utilized as inputs. The three-layer perceptron neural network models with the best performance were chosen by testing with different numbers of neurons in the hidden layer and different activation functions. The mean bias error, mean absolute error, root mean squared error, correlation coefficient, and index of agreement or Willmott's Index for the validation data for 1hO₃ forecasting were 0.005 μg m^-3, 12.149 μg m^-3, 15.926 μg m^-3, 0.988, and 0.950, respectively, for the traffic station (Dnevnik), and - 0.565 μg m^-3, 10.101 μg m^-3, 12.962 μg m^-3, 0.911, and 0.953, respectively, for the background station (Liman). For 8hO₃ forecasting, statistical indicators were - 1.126 μg m^-3, 10.614 μg m^-3, 12.962 μg m^-3, 0.910, and 0.948 respectively for the station Dnevnik and - 0.001 μg m^-3, 8.574 μg m^-3, 10.741 μg m^-3, 0.936, and 0.966, respectively, for the station Liman. According to the Kolmogorov-Smirnov test, there is no significant difference between measured and predicted data. Models showed a good performance in forecasting days with the high values over a certain threshold.

New Multiphase Chemical Processes Influencing Atmospheric Aerosols, Air Quality, and Climate in the Anthropocene

Atmospheric aerosols and fine particulate matter (PM_2.5) are strongly affecting human health and climate in the Anthropocene, that is, in the current era of globally pervasive and rapidly increasing human influence on planet Earth. Poor air quality associated with high aerosol concentrations is among the leading health risks worldwide, causing millions of attributable excess deaths and years of life lost every year. Besides their health impact, aerosols are also influencing climate through interactions with clouds and solar radiation with an estimated negative total effective radiative forcing that may compensate about half of the positive radiative forcing of carbon dioxide but exhibits a much larger uncertainty. Heterogeneous and multiphase chemical reactions on the surface and in the bulk of solid, semisolid, and liquid aerosol particles have been recognized to influence aerosol formation and transformation and thus their environmental effects. However, atmospheric multiphase chemistry is not well understood because of its intrinsic complexity of dealing with the matter in multiple phases and the difficulties of distinguishing its effect from that of gas phase reactions.

Recently, research on atmospheric multiphase chemistry received a boost from the growing interest in understanding severe haze formation of very high PM_2.5 concentrations in polluted megacities and densely populated regions. State-of-the-art models suggest that the gas phase reactions, however, are not capturing the high concentrations and rapid increase of PM_2.5 observed during haze events, suggesting a gap in our understanding of the chemical mechanisms of aerosol formation. These haze events are characterized by high concentrations of aerosol particles and high humidity, especially favoring multiphase chemistry. In this Account, we review recent advances that we have made, as well as current challenges and future perspectives for research on multiphase chemical processes involved in atmospheric aerosol formation and transformation. We focus on the following questions: what are the key reaction pathways leading to aerosol formation under polluted conditions, what is the relative importance of multiphase chemistry versus gas-phase chemistry, and what are the implications for the development of efficient and reliable air quality control strategies? In particular, we discuss advances and challenges related to different chemical regimes of sulfate, nitrate, and secondary organic aerosols (SOAs) under haze conditions, and we synthesize new insights into the influence of aerosol water content, aerosol pH, phase state, and nanoparticle size effects. Overall, there is increasing evidence that multiphase chemistry plays an important role in aerosol formation during haze events. In contrast to the gas phase photochemical reactions, which are self-buffered against heavy pollution, multiphase reactions have a positive feedback mechanism, where higher particle matter levels accelerate multiphase production, which further increases the aerosol concentration resulting in a series of record-breaking pollution events. We discuss perspectives to fill the gap of the current understanding of atmospheric multiphase reactions that involve multiple physical and chemical processes from bulk to nanoscale and from regional to global scales. A synthetic approach combining laboratory experiments, field measurements, instrument development, and model simulations is suggested as a roadmap to advance future research.

The impact of synoptic patterns on summertime ozone pollution in the North China Plain

Surface ozone pollution is a challenging environmental issue in most parts of China. In particular, the North China Plain (NCP) region suffers from the severest ozone pollution throughout the country. In addition to the emission of precursors, ozone concentration is closely related to meteorological conditions resulting from regional atmospheric circulation. In this study, we investigate the relationship between synoptic patterns and summertime ozone pollution in the NCP using the objective principal component analysis in T-mode (T-PCA) classification method. Four dominant synoptic patterns are identified during the summers of 2014-2018. The heaviest ozone pollution is found to be associated with a high pressure anomaly over the Northwest Pacific and a distinct low pressure center in Northeast China. The southwesterly wind surrounding the low pressure center brings dry, warm air from inland South China, resulting in a high temperature, low humidity environment in the NCP, which favors the chemical formation of surface ozone. Locally, this type is associated with a moderate planetary boundary layer height (PBLH) of ~860 m and a stronger warm anomaly within the boundary layer than the upper level. We also notice a non-linear relationship between surface ozone concentration and the PBLH, i.e., ozone concentration first increases with PBLH till ~0.9 km, and then remains stable. This initial increase may relate to enhanced mixing with upper levels where ozone concentration is typically higher than that near the surface. However, when PBLH further increases, this downward mixing effect is balanced with the stronger upward turbulent mixing so that surface ozone shows little change. The synoptic patterns identified here, however, is unlikely responsible for the observed increasing trend in ozone concentration over the NCP region. Our study sheds light on the meteorological contribution to surface ozone pollution in North China and provides a reference for the pollution control and prediction.

OH radicals reactivity towards phenol-related pollutants in water: temperature dependence of the rate constants and novel insights into the [OH-phenol]˙ adduct formation

Substituted phenols are known to readily react with the hydroxyl radical (OH˙), which is the most powerful atmospheric oxidant and is also most often used in advanced oxidation processes (AOP) for wastewater treatment. We report temperature-dependent (278.15-318.15 K) second order kinetic rate constants for the aqueous-phase reactions of OH˙ with phenol and four substituted phenols: catechol, phloroglucinol, pyrogallol and 3-methylcatechol, with the last two measured for the first time. The constructed Hammett plots for mono- and di-substituted phenols have the potential to be further applied for predicting the reaction rate constants of other substituted phenols at 298.15 K. This will significantly facilitate the optimization of AOP and improve the predictive capabilities of atmospheric multiphase models in the future. Moreover, an advancement in the understanding of the underlying mechanism, i.e. OH˙ addition to the aromatic ring is made by theoretical calculations at the M06-2X level. We demonstrate that the position of substituents on the aromatic ring is important for the [OH-phenol]˙ adduct formation, which is supported by the experiment and theoretical calculations. Adjacent and nonadjacent electron donor/acceptor substituents differently impact the interplay between the activation energy and entropy. We also show that explicit solvation has to be accounted for in theoretical models in order to explicitly describe the formation of the transition state.

The Atmosphere

The atmosphere is composed of nitrogen, oxygen and argon, a variety of trace gases, and particles or aerosols from a variety of sources. Reactive, trace gases have short mean residence time in the atmosphere and large spatial and temporal variations in concentration. Many trace gases are removed by reaction with hydroxyl radical and deposition in rainfall or dryfall at the Earth's surface. The upper atmosphere, the stratosphere, contains ozone that screens ultraviolet light from the Earth's surface. Chlorofluorocarbons released by humans lead to the loss of stratospheric ozone, which might eventually render the Earth's land surface uninhabitable. Changes in the composition of the atmosphere, especially rising concentrations of CO₂, CH₄, and N₂O, will lead to climatic changes over much of the Earth's surface.

Photoacoustics of single laser-trapped nanodroplets for the direct observation of nanofocusing in aerosol photokinetics

Photochemistry taking place in atmospheric aerosol droplets has a significant impact on the Earth's climate. Nanofocusing of electromagnetic radiation inside aerosols plays a crucial role in their absorption behaviour, since the radiation flux inside the droplet strongly affects the activation rate of photochemically active species. However, size-dependent nanofocusing effects in the photokinetics of small aerosols have escaped direct observation due to the inability to measure absorption signatures from single droplets. Here we show that photoacoustic measurements on optically trapped single nanodroplets provide a direct, broadly applicable method to measure absorption with attolitre sensitivity. We demonstrate for a model aerosol that the photolysis is accelerated by an order of magnitude in the sub-micron to micron size range, compared with larger droplets. The versatility of our technique promises broad applicability to absorption studies of aerosol particles, such as atmospheric aerosols where quantitative photokinetic data are critical for climate predictions.

Analysis of surface ozone using a recurrent neural network

Hourly concentrations of ozone (O₃) and nitrogen dioxide (NO₂) have been measured for 16 years, from 1998 to 2013, in a seaside town in central Italy. The seasonal trends of O₃ and NO₂ recorded in this period have been studied. Furthermore, we used the data collected during one year (2005), to define the characteristics of a multiple linear regression model and a neural network model. Both models are used to model the hourly O₃ concentration, using, two scenarios: 1) in the first as inputs, only meteorological parameters and 2) in the second adding photochemical parameters at those of the first scenario. In order to evaluate the performance of the model four statistical criteria are used: correlation coefficient, fractional bias, normalized mean squared error and a factor of two. All the criteria show that the neural network gives better results, compared to the regression model, in all the model scenarios. Predictions of O₃ have been carried out by many authors using a feed forward neural architecture. In this paper we show that a recurrent architecture significantly improves the performances of neural predictors. Using only the meteorological parameters as input, the recurrent architecture shows performance better than the multiple linear regression model that uses meteorological and photochemical data as input, making the neural network model with recurrent architecture a more useful tool in areas where only weather measurements are available. Finally, we used the neural network model to forecast the O₃ hourly concentrations 1, 3, 6, 12, 24 and 48 h ahead. The performances of the model in predicting O₃ levels are discussed. Emphasis is given to the possibility of using the neural network model in operational ways in areas where only meteorological data are available, in order to predict O₃ also in sites where it has not been measured yet.

Establishment of a structural equation model for ground-level ozone: a case study at an urban roadside site

This study established a cause-effect relationship between ground-level ozone and latent variables employing partial least-squares analysis at an urban roadside site in four distinct seasons. Two multivariate analytic methods, factor analysis, and cluster analysis were adopted to cite and identify suitable latent variables from 14 observed variables (i.e., meteorological factors, wind and primary air pollutants) in 2008-2010. Analytical results showed that the first six components explained 80.3 % of the variance, and eigenvalues of the first four components were greater than 1. The effectiveness of this model was empirically confirmed with three indicators. Except for surface pressure, factor loadings of observed variables were 0.303-0.910 and reached statistical significance at the 5 % level. Composite reliabilities for latent variables were 0.672-0.812 and average variances were 0.404-0.547, except for latent variable "primary" in spring; thus, discriminant validity and convergent validity were marginally accepted. The developed model is suitable for the assessment of urban roadside surface ozone, considering interactions among meteorological factors, wind factors, and primary air pollutants in each season.

Coupled-cluster and multireference configuration interaction study of the low-lying excited states of the H2O2-H2O complex

Ab initio molecular orbital methods have been employed to determine the molecular structure, vibrational frequencies, and stability of the H(2)O(2)-H(2)O complex. These parameters were determined using coupled-cluster methods with Dunning correlation consistent basis sets. Vertical excitation energies for the lowest four excited states are calculated with the multireference internally contracted configuration interaction methods and coupled cluster equation of motion methods. No significant difference was observed between the first vertical excited state energies of H(2)O(2) within the H(2)O(2)-H(2)O complex.

A tunable diode laser absorption spectrometer for formaldehyde atmospheric measurements validated by simulation chamber instrumentation

A tunable diode laser absorption spectrometer (TDLAS) for formaldehyde atmospheric measurements has been set up and validated through comparison experiments with a Fourier transform infrared spectrometer (FT-IR) in a simulation chamber. Formaldehyde was generated in situ in the chamber from reaction of ethene with ozone. Three HCHO ro-vibrational line intensities (at 2909.71, 2912.09 and 2914.46 cm(-1)) possibly used by TDLAS were calibrated by FT-IR spectra simultaneously recorded in the 1600-3200 cm(-1) domain during ethene ozonolysis, enabling the on-line deduction of the varying concentration for HCHO in formation. The experimental line intensities values inferred confirmed the calculated ones from the updated HITRAN database. In addition, the feasibility of stratospheric in situ HCHO measurements using the 2912.09 cm(-1) line was demonstrated. The TDLAS performances were also assessed, leading to a 2sigma detection limit of 88 ppt in volume mixing ratio with a response time of 60 sec at 30 Torr and 294 K for 112 m optical path. As part of this work, the room-temperature rate constant of this reaction and the HCHO formation yield were found to be in excellent agreement with the compiled literature data.

Evidence for Photochemical Formation of H2O2 and Oxidation of SO2 in Authentic Fog Water

When samples of rain and fog water were exposed to ultraviolet and visible light, reactive transients such as hydrogen peroxide were formed and dissolved organic matter and sulfur dioxide were depleted. These results, in conjunction with those from previous studies, imply that dissolved organic compounds and transition metals such as iron ions are involved in the photochemical formation of hydrogen peroxide and other photooxidants in atmospheric waters.