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Hybrid approaches combining machine learning with traditional inverse problem solution methods represent a promising direction for the further development of inverse modeling algorithms. The paper proposes an approach to emission source identification from measurement data for advection–diffusion–reaction models. The approach combines general-type source identification and post-processing refinement: first, emission source identification by measurement data is carried out by a sensitivity operator-based algorithm, and then refinement is done by incorporating a priori information about unknown sources. A general-type distributed emission source identified at the first stage is transformed into a localized source consisting of multiple point-wise sources. The second, refinement stage consists of two steps: point-wise source localization and emission rate estimation. Emission source localization is carried out using deep learning with convolutional neural networks. Training samples are generated using a sensitivity operator obtained at the source identification stage. The algorithm was tested in regional remote sensing emission source identification scenarios for the Lake Baikal region and was able to refine the emission source reconstruction results. Hence, the aggregates used in traditional inverse problem solution algorithms can be successfully applied within machine learning frameworks to produce hybrid algorithms. [ABSTRACT FROM AUTHOR]

Air quality data sets are widely used in numerous analyses. Missing values are ubiquitous in air quality data sets as the data are collected through sensors. Recovery of missing data is a challenging task in the data preprocessing stage. This task becomes more challenging in time series data as time is an implicit variable that cannot be ignored. Even though existing methods to deal with missing data in time series perform well in situations where the percentage of missing values is relatively low and the gap size is small, their performances are reasonably lower when it comes to large gaps. This paper presents a novel algorithm based on seasonal decomposition and elastic net regression to impute large gaps of time series data when there exist correlated variables. This method outperforms several other existing univariate approaches namely Kalman smoothing on ARIMA models, Kalman smoothing on structural time series models, linear interpolation, and mean imputation in imputing large gaps. However, this is applicable only when there exists one or more correlated variables with the time series with large gaps. [ABSTRACT FROM AUTHOR]

In the last decade, we have seen drastic changes in the air pollution level, which has become a critical environmental issue. It should be handled carefully towards making the solutions for proficient healthcare. Reducing the impact of air pollution on human health is possible only if the data is correctly classified. In numerous classification problems, we are facing the class imbalance issue. Learning from imbalanced data is always a challenging task for researchers, and from time to time, possible solutions have been developed by researchers. In this paper, we are focused on dealing with the imbalanced class distribution in a way that the classification algorithm will not compromise its performance. The proposed algorithm is based on the concept of the adjusting kernel scaling (AKS) method to deal with the multi-class imbalanced dataset. The kernel function's selection has been evaluated with the help of weighting criteria and the chi-square test. All the experimental evaluation has been performed on sensor-based Indian Central Pollution Control Board (CPCB) dataset. The proposed algorithm with the highest accuracy of 99.66% wins the race among all the classification algorithms i.e. Adaboost (59.72%), Multi-Layer Perceptron (95.71%), GaussianNB (80.87%), and SVM (96.92). The results of the proposed algorithm are also better than the existing literature methods. It is also clear from these results that our proposed algorithm is efficient for dealing with class imbalance problems along with enhanced performance. Thus, accurate classification of air quality through our proposed algorithm will be useful for improving the existing preventive policies and will also help in enhancing the capabilities of effective emergency response in the worst pollution situation. [ABSTRACT FROM AUTHOR]

The aim of this paper is to introduce a NUT model (NUT: network-uncertainty-trust) that aids the decrease of the uncertainty of measurements in autonomous hybrid Internet of Things sensor networks. The problem of uncertainty in such networks is a consequence of various operating conditions and varied quality of measurement nodes, making statistical approach less successful. This paper presents a model for decreasing the uncertainty through the use of socially inspired metaphors of reputation, trust, and confidence that are the untapped latent information. The model described in the paper shows how the individual reputation of each node can be assessed on the basis of opinions provided by other nodes of the hybrid measurement network, and that this method allows to assess the extent of uncertainty the node introduces to the network. This, in turn, allows nodes of low uncertainty to have a greater impact on the reconstruction of values. The verification of the model, as well as examples of its applicability to air quality measurements are presented as well. Simulations demonstrate that the use of the model can decrease the uncertainty by up to 55% while using the EWMA (exponentially weighted moving average) algorithm, as compared to the reference one. [ABSTRACT FROM AUTHOR]

Although studies have increasingly linked air pollution to specific health outcomes, less well understood is how public perceptions of air quality respond to changing pollutant levels. The growing availability of air pollution measurements and the proliferation of social media provide an opportunity to gauge public discussion of air quality conditions. In this paper, we consider particulate matter (PM) measurements from four Chinese megacities (Beijing, Shanghai, Guangzhou, and Chengdu) together with 112 million posts on Weibo (a popular Chinese microblogging system) from corresponding days in 2011–2013 to identify terms whose frequency was most correlated with PM levels. These correlations are used to construct an Air Discussion Index (ADI) for estimating daily PM based on the content of Weibo posts. In Beijing, the Chinese city with the most PM as measured by U.S. Embassy monitor stations, we found a strong correlation (R = 0.88) between the ADI and measured PM. In other Chinese cities with lower pollution levels, the correlation was weaker. Nonetheless, our results show that social media may be a useful proxy measurement for pollution, particularly when traditional measurement stations are unavailable, censored or misreported. [ABSTRACT FROM AUTHOR]

Several studies have been carried out over the past 20 or so years to assess the level of visual air quality that is judged to be acceptable in urban settings. Groups of individuals were shown slides or computer-projected scenes under a variety of haze conditions and asked to judge whether each image represented acceptable visual air quality. The goal was to assess the level of haziness found to be acceptable for purposes of setting an urban visibility regulatory standard. More recently, similar studies were carried out in Beijing, China, and the more pristine Grand Canyon National Park and Great Gulf Wilderness. The studies clearly showed that when preference ratings were compared to measures of atmospheric haze such as atmospheric extinction, visual range, or deciview (dv), there was not a single indicator that represented acceptable levels of visual air quality for the varied urban or more remote settings. For instance, using a Washington, D.C., setting, 50% of the observers rated the landscape feature as not having acceptable visual air quality at an extinction of 0.19 km−1 (21 km visual range, 29 dv), while the 50% acceptability point for a Denver, Colorado, setting was 0.075 km−1 (52 km visual range, 20 dv) and for the Grand Canyon it was 0.023 km−1 (170 km visual range, 7 dv). Over the past three or four decades, many scene-specific visibility indices have been put forth as potential indicators of visibility levels as perceived by human observers. They include, but are not limited to, color and achromatic contrast of single landscape features, average and equivalent contrast of the entire image, edge detection algorithms such as the Sobel index, and just-noticeable difference or change indexes. This paper explores various scene-specific visual air quality indices and examines their applicability for use in quantifying visibility preference levels and judgments of visual air quality. Implications: Visibility acceptability studies clearly show that visibility become more unacceptable as haze increases. However, there are large variations in the preference levels for different scenes when universal haze indicators, such as atmospheric extinction, are used. This variability is significantly reduced when the sky-landscape contrast of the more distant landscape features in the observed scene is used. Analysis suggest that about 50% of individuals would find the visibility unacceptable if at any time the more distant landscape features nearly disappear, that is, they are at the visual range. This common metric could form the basis for setting an urban visibility standard. [ABSTRACT FROM AUTHOR]

Today, China is facing a very serious issue of Air Pollution due to its dreadful impact on the human health as well as the environment. The urban cities in China are the most affected due to their rapid industrial and economic growth. Therefore, it is of extreme importance to come up with new, better and more reliable forecasting models to accurately predict the air quality. This paper selected Beijing, Tianjin and Shijiazhuang as three cities from the Jingjinji Region for the study to come up with a new model of collaborative forecasting using Support Vector Regression (SVR) for Urban Air Quality Index (AQI) prediction in China. The present study is aimed to improve the forecasting results by minimizing the prediction error of present machine learning algorithms by taking into account multiple city multi-dimensional air quality information and weather conditions as input. The results show that there is a decrease in MAPE in case of multiple city multi-dimensional regression when there is a strong interaction and correlation of the air quality characteristic attributes with AQI. Also, the geographical location is found to play a significant role in Beijing, Tianjin and Shijiazhuang AQI prediction. [ABSTRACT FROM AUTHOR]

Understanding the boundary-layer height and its dynamics is crucial for a wide array of applications spanning various fields. Accurate identification of the boundary-layer top contributes to improved air quality predictions, pollutant transport assessments, and enhanced numerical weather prediction through parameterization and assimilation techniques. Despite its significance, defining and observing the boundary-layer top remains challenging. Existing methods of estimating the boundary-layer height encompass radiosonde-based methods, radar-based retrievals, and more. As emerging boundary-layer observation platforms emerge, it is useful to reevaluate the efficacy of existing boundary-layer top detection methods and explore new ones. This study introduces a fuzzy-logic algorithm that leverages the synergy of multiple remote-sensing boundary-layer profiling instruments. By harnessing the distinct advantages of each sensing platform, the proposed method enables accurate boundary-layer height estimation both during daytime and nocturnal conditions. The algorithm is benchmarked against radiosonde-derived boundary-layer top estimates obtained from balloon launches across diverse locations in Wisconsin, Oklahoma, and Louisiana during summer and fall. The findings reveal notable similarities between the results produced by the proposed fuzzy-logic algorithm and traditional radiosonde-based approaches. However, this study delves into the nuanced differences in their behavior, providing insightful analyses about the underlying causes of the observed discrepancies. The fuzzy-logic boundary-layer top detection algorithm, called BLISS-FL, is released publicly fostering collaboration and advancement within the research community. [ABSTRACT FROM AUTHOR]

It is important to be able to predict high concentrations of tropospheric ozone and to inform the population about any violations of air-quality standards, as defined by international regulations. Although first-principle models that cover large geographical regions and different atmospheric layers are improving constantly, they typically still only cover geographical regions with a relatively low resolution. Such model predictions can be problematic for the micro-locations of a complex terrain, i.e., a terrain with a large geographical diversity or urban terrain. For such micro-locations, statistical models can be utilised. This paper presents a modelling and prediction algorithm that can be used in, or in accordance with, a mobile air-quality measurement station. Such a mobile station would enable the set-up of a statistical model and a relatively rapid access to the model's predictions for a specific geographical micro-location without a large quantity of historical of measurements. Uncertainty information about the model's predictions is also usually required. In addition, such a model can adapt to long-term changes, such as climate changes. In the paper we propose Gaussian-process models for the described modelling and prediction. In particular, we selected evolving Gaussian-process models that update on-line with the incoming measurement data. The proposed algorithm for the mobile air-quality measurement and the forecasting station is evaluated on measurements from five locations in Slovenia with different topographical and geographical properties. The obtained evaluation results confirm the feasibility of the concept. [ABSTRACT FROM AUTHOR]

Air pollution in many countries is worsening with industrialization and urbanization, resulting in climate change and affecting people's health, thus, making the work of policymakers more difficult. It is therefore both urgent and necessary to establish amore scientific air quality monitoring and early warning system to evaluate the degree of air pollution objectively, and predict pollutant concentrations accurately. However, the integration of air quality assessment and air pollutant concentration prediction to establish an air quality system is not common. In this paper, we propose a new air quality monitoring and early warning system, including an assessment module and forecasting module. In the air quality assessment module, fuzzy comprehensive evaluation is used to determine the main pollutants and evaluate the degree of air pollution more scientifically. In the air pollutant concentration prediction module, a novel hybridization model combining complementary ensemble empirical mode decomposition, a modified cuckoo search and differential evolution algorithm, and an Elman neural network, is proposed to improve the forecasting accuracy of six main air pollutant concentrations. To verify the effectiveness of this system, pollutant data for two cities in China are used. The result of the fuzzy comprehensive evaluation shows that the major air pollutants in Xi'an and Jinan are PM 10 and PM 2.5 respectively, and that the air quality of Xi'an is better than that of Jinan. The forecasting results indicate that the proposed hybrid model is remarkably superior to all benchmark models on account of its higher prediction accuracy and stability. [ABSTRACT FROM AUTHOR]

Studies on air quality frequently adopt clustering, in particular the k-means technique, owing to its simplicity, ease of implementation and efficiency. The aim of the present paper was the assessment of air quality in a winter season (December–February) in the conditions of temperature inversion using the k-means method, representing a non-hierarchical algorithm of cluster analysis. The air quality was assessed on the basis of the concentrations of particulate matter (PM10, PM2.5). The studies were conducted in four winter seasons (2015/16, 2016/17, 2017/18, 2019/20) in Wrocław (Poland). As a result of the application of the v-fold cross test, six clusters for each fraction of PM were identified. Even though the analysis covers only four winter seasons, the applied method has unequivocally revealed that the characteristics of surface-based (SBI) and elevated inversions (ELI) affect the concentration level of both fractions of particulate matter. In the case of PM10, the average lowest daily concentration (15.5 µg·m−3) was recorded in the conditions of approx. 205 m in thickness, 0.5 °C intensity of the SBI and at the height of the base of the ELI at approx. 1700 m a.g.l., a thickness of 148 m and an intensity of 1.2 °C. In turn, the average highest concentration (136 µg·m−3) was recorded at a thickness of SBI of approx. 400 m and an intensity of 1.4 °C. Such high concentration occurred when the lowest location of ELI formed at 764 m a.g.l. with a thickness of 308 m and an intensity of 0.96 °C. A marked role of the thickness of the SBI and ELI as well as the height of the base of the lowest location of ELI was also manifested with respect to PM2.5 concentrations. [ABSTRACT FROM AUTHOR]

Crowdsensing offers an efficient way to meet the demand in large-scale sensing applications. In crowdsensing, optimal task allocation is challenging since sensing tasks with different requirements of quality of sensing are typically associated with specific locations, and mobile users have time constraints. We show that the allocation problem is NP-hard. We then focus on approximation algorithms and devise an efficient local-ratio-based algorithm (LRBA). Our analysis shows that the approximation ratio of the aggregate rewards obtained by optimal allocation to those by LRBA is 5. This reveals that LRBA is efficient, since a lower (but not tight) bound on the approximation ratio is 4. We extend the results to the general scenario where mobile users are heterogeneous. A distributed version of LRBA, namely DLRBA, is designed, which can be iteratively executed at each mobile user without the need for the platform to collect all the information of mobile users. We prove that both centralized and distributed versions can output the same solution. Extensive simulation results are provided to demonstrate the advantages of our proposed algorithms. [ABSTRACT FROM PUBLISHER]

Cloud shadows are observed by the TROPOMI satellite instrument as a result of its high spatial resolution compared to its predecessor instruments. These shadows contaminate TROPOMI's air quality measurements, because shadows are generally not taken into account in the models that are used for aerosol and trace gas retrievals. If the shadows are to be removed from the data, or if shadows are to be studied, an automatic detection of the shadow pixels is needed. We present the Detection AlgoRithm for CLOud Shadows (DARCLOS) for TROPOMI, which is the first cloud shadow detection algorithm for a spaceborne spectrometer. DARCLOS raises potential cloud shadow flags (PCSFs), actual cloud shadow flags (ACSFs), and spectral cloud shadow flags (SCSFs). The PCSFs indicate the TROPOMI ground pixels that are potentially affected by cloud shadows based on a geometric consideration with safety margins. The ACSFs are a refinement of the PCSFs using spectral reflectance information of the PCSF pixels and identify the TROPOMI ground pixels that are confidently affected by cloud shadows. Because we find indications of the wavelength dependence of cloud shadow extents in the UV, the SCSF is a wavelength-dependent alternative for the ACSF at the wavelengths of TROPOMI's air quality retrievals. We validate the PCSF and ACSF with true-colour images made by the VIIRS instrument on board Suomi NPP orbiting in close proximity to TROPOMI on board Sentinel-5P. We find that the cloud evolution during the overpass time difference between TROPOMI and VIIRS complicates this validation strategy, implicating that an alternative cloud shadow detection approach using co-located VIIRS observations could be problematic. We conclude that the PCSF can be used to exclude cloud shadow contamination from TROPOMI data, while the ACSF and SCSF can be used to select pixels for the scientific analysis of cloud shadow effects. [ABSTRACT FROM AUTHOR]

Concentrations of particulate aerosols and their vertical placement in the atmosphere determine their interaction with the Earth system and their impact on air quality. Space-based lidar, such as the Cloud–Aerosol Transport System (CATS) technology demonstration instrument, is well-suited for determining the vertical structure of these aerosols and their diurnal cycle. Through the implementation of aerosol-typing algorithms, vertical layers of aerosols are assigned a type, such as marine, dust, and smoke, and a corresponding extinction-to-backscatter (lidar) ratio. With updates to the previous aerosol-typing algorithms, we find that CATS, even as a technology demonstration, observed the documented seasonal cycle of aerosols, comparing favorably with the Cloud–Aerosol Lidar with Orthogonal Polarization (CALIOP) space-based lidar and the NASA Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2) model reanalysis. By leveraging the unique orbit of the International Space Station, we find that CATS can additionally resolve the diurnal cycle of aerosol altitude as observed by ground-based instruments over the Maritime Continent of Southeast Asia. [ABSTRACT FROM AUTHOR]

Highly resolved pollution maps are a valuable resource for many issues related to air quality including exposure modelling and urban planning. We present an approach for their generation based on data from a mobile sensor network and statistical modelling. An extensive record of particle number concentrations (PNCs) spanning more than 1.5 years was compiled by the tram-based OpenSense mobile sensor network in the City of Zurich. The sensor network consists of 10 sensor nodes installed on the roof of trams operating on different services according to their regular operation schedules. We developed a statistical modelling approach based on Generalized Additive models (GAMs) utilizing the PNC data obtained along the tram tracks as well as georeferenced information as predictor variables. Our approach includes a variable selection algorithm to ensure that individual models rely on the optimal set of predictor variables. Our models have high temporal and spatial resolutions of 30 min and 10 m by 10 m, respectively, and allow the spatial prediction of PNC in the municipal area of Zurich. We applied our approach to PNC data from two dedicated time periods: July–Sept. 2013 and Dec. 2013–Feb. 2014. The models strongly rely on traffic related predictor variables (vehicle counts) and, due to the hilly topography of Zurich, on elevation. We assessed the model performance by leave-one-out cross-validation and by comparing PNC predictions to measurements at fixed reference sites and to PNC measurements obtained by pedestrians. Model predictions reproduce well the main features of the PNC field in environment types similar to those passed by individual trams. Model performance is worse at elevated background locations probably due to the weak coverage of similar spots by the tram network. We end the paper by outlining a route finding algorithm which utilizes the highly resolved PNC maps providing the exposure minimal route for cyclists. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the relationship between urban spatial structure and air quality in the United States. By using urban landscape metrics framework, we empirically examine whether fragmentary and sprawling urban patterns are associated with low air quality. We develop an algorithm to correct for biases within the urban landscape metrics in the United States. Controlling for demographic variables and economic activity, we find a strong relationship between the type and pattern of development and pollutant levels. The finding is not biased by the presence of relatively rural counties in the dataset suggesting that paying close attention to the urban form might have some implications for air quality. [ABSTRACT FROM AUTHOR]

The coarseModerate Resolution Imaging Spectroradiometer (MODIS) aerosol optical depth (AOD) product (spatial resolution: 3 km) retrieved by the dark-target algorithm always generates the missing values when being adopted to estimate the ground-level PM2.5 concentrations. In this study, we developed a two-stage random forest using MODIS 3-km AOD to obtain the PM2.5 concentrations with full coverage in a contiguous coastal developed region, i.e., Yangtze River delta--Fujian--Pearl River delta (YRD--FJ--PRD) region of China. A first-stage random forest--integrated six meteorological fields was employed to predict the missing values of AOD product, and the combined AOD (i.e., random forest--derived AOD and MODIS 3-km AOD) incorporated with other ancillary variables were developed for predicting PM2.5 concentrations within a second-stage random forest model. The results showed that the first-stage random forest could explain 94% of the AOD variability over YRD--FJ--PRD region, and we achieved a site-based cross validation (CV) R² of 0.87 and a time-based CV R² of 0.85. The full-coverage PM2.5 concentrations illustrated a spatial pattern with annual-mean PM2.5 of 46, 40, and 35 µg m-3 in YRD, PRD, and FJ, respectively, sharing the same trend with previous studies. Our results indicated that the proposed two-stage random forest model could be effectively used for PM2.5 estimation in different areas. [ABSTRACT FROM AUTHOR]

The widely used Moderate Resolution Imaging Spectroradiometer (MODIS) Dark‐Target (DT) aerosol product fails to accurately retrieve Aerosol Optical Depth (AOD) over shallow and turbid Coastal Waters (CWs). To fill in gaps, and to improve land to ocean AOD continuity, we developed a coastal water retrieval algorithm at a spatial resolution of 1 km (CW‐1km). CW‐1km relies on observed top‐of‐atmosphere reflectance at 2.1 μm (ρ2.1), both to derive AOD and to perform a spatial variation test that enhances the existing DT masks for clouds and land. We show that the CW‐1km improves spatial continuity of AOD between land, coast, and open ocean, while also increasing AOD product availability by 47.0%. Comparing with 15 years of marine aerosol network measurements, CW‐1km AODs are validated to have a normalized mean bias of 1.0%, which is much smaller than 17.6% for the original DT product. Plain Language Summary: The Moderate Resolution Imaging Spectroradiometer (MODIS)‐retrieved Aerosol Optical Depth (AOD) products have been widely used by the research community to study aerosol impacts on air quality, weather, and climate changes. However, AOD products are often missing or highly biased over Coastal Waters (CWs), due to CW being turbid, shallow, and mixed with the land. Here, we have created a new algorithm (CW‐1km) that retrieves AOD with a spatial resolution of 1 km. This new CW algorithm greatly improves spatial coverage and provides a smooth AOD transition from land to coastal to open waters and does not shown the spatial gaps and erroneous values as found in the MODIS AOD products over CW. The improvement in both accuracy and retrieval coverage can further enhance air quality and climate studies in coastal regions, which is also where over 60% of global human population reside. Our CW algorithm can be ported to existing and future satellite sensors to improve the retrieval of aerosol distribution over coastal and estuarine water areas. Key Points: The Coastal Water (CW) algorithm for Moderate Resolution Imaging Spectroradiometer Aerosol Optical Depth (AOD) retrieval at 1 km over shallow and turbid CW is developedThe 2.1‐μm Top‐of‐Atmosphere reflectance spatial variation test improves the land mask near coastline and cloud mask over turbid CWCW‐1km AOD agrees with in situ measurements, increases availability, and removes the artifacts of AOD from land to open ocean [ABSTRACT FROM AUTHOR]

The social quality of life (SQOL) for single-family housing residents can be improved by enhancing their thermal comfort, indoor lighting quality, indoor air quality, and locating the housing unit in high-quality and safe neighborhoods. Accomplishing these SQOL improvements often leads to an increase in the life-cycle cost (LCC) of the housing unit. This paper presents a multiobjective optimization model that is capable of generating optimal trade-offs between the two conflicting objectives of maximizing the SQOL for single-family housing residents and minimizing the LCC of single-family housing units. The model is designed to maximize the SQOL for housing residents by improving their thermal comfort, indoor lighting quality, indoor air quality, and neighborhood quality. The model is also designed to minimize the LCC of housing units by minimizing the initial housing unit cost as well as its annual energy, utility, and maintenance costs. The model is developed in three main stages that focus on (1) identifying relevant criteria and metrics to measure the performance of these two optimization objectives; (2) modeling all decision variables, objective functions, and constraints; and (3) implementing a multiobjective genetic algorithm model to evaluate its performance. An application example of a housing unit is analyzed to illustrate the use of the developed model and demonstrate its capabilities in identifying optimal configurations of single-family housing design and construction decisions. [ABSTRACT FROM AUTHOR]

Air quality measures that were implemented in Europe in the 1990s resulted in reductions of ozone precursor concentrations. In this study, the effect of these reductions on ozone is investigated by analyzing surface measurements of this pollutant for the time period between 2000 and 2015. Using a nonparametric timescale decomposition methodology, the long-term, seasonal and short-term variation in ozone observations were extracted. A clustering algorithm was applied to the different timescale variations, leading to a classification of sites across Europe based on the temporal characteristics of ozone. The clustering based on the long-term variation resulted in a site-type classification, while a regional classification was obtained based on the seasonal and short-term variations. Long-term trends of deseasonalized mean and meteo-adjusted peak ozone concentrations were calculated across large parts of Europe for the time period 2000–2015. A multidimensional scheme was used for a detailed trend analysis, based on the identified clusters, which reflect precursor emissions and meteorological influence either on the inter-annual or the short-term timescale. Decreasing mean ozone concentrations at rural sites and increasing or stabilizing at urban sites were observed. At the same time, downward trends for peak ozone concentrations were detected for all site types. In addition, a reduction of the amplitude in the seasonal cycle of ozone and a shift in the occurrence of the seasonal maximum towards earlier time of the year were observed. Finally, a reduced sensitivity of ozone to temperature was identified. It was concluded that long-term trends of mean and peak ozone concentrations are mostly controlled by precursor emissions changes, while seasonal cycle trends and changes in the sensitivity of ozone to temperature are among other factors driven by regional climatic conditions. [ABSTRACT FROM AUTHOR]

The California Regional Multisector Air Quality Emissions (CA-REMARQUE) model is developed to predict changes to criteria pollutant emissions inventories in California in response to sophisticated emissions control programs implemented to achieve deep greenhouse gas (GHG) emissions reductions. Two scenarios for the year 2050 act as the starting point for calculations: a business-as-usual (BAU) scenario and an 80% GHG reduction (GHG-Step) scenario. Each of these scenarios was developed with an energy economic model to optimize costs across the entire California economy and so they include changes in activity, fuels, and technology across economic sectors. Separate algorithms are developed to estimate emissions of criteria pollutants (or their precursors) that are consistent with the future GHG scenarios for the following economic sectors: (i) on-road, (ii) rail and off-road, (iii) marine and aviation, (iv) residential and commercial, (v) electricity generation, and (vi) biorefineries. Properly accounting for new technologies involving electrification, biofuels, and hydrogen plays a central role in these calculations. Critically, criteria pollutant emissions do not decrease uniformly across all sectors of the economy. Emissions of certain criteria pollutants (or their precursors) increase in some sectors as part of the overall optimization within each of the scenarios. This produces nonuniform changes to criteria pollutant emissions in close proximity to heavily populated regions when viewed at 4 km spatial resolution with implications for exposure to air pollution for those populations. As a further complication, changing fuels and technology also modify the composition of reactive organic gas emissions and the size and composition of particulate matter emissions. This is most notably apparent through a comparison of emissions reductions for different size fractions of primary particulate matter. Primary PM2:5 emissions decrease by 4% in the GHG-Step scenario vs. the BAU scenario while corresponding primary PM0:1 emissions decrease by 36%. Ultrafine particles (PM0:1) are an emerging pollutant of concern expected to impact public health in future scenarios. The complexity of this situation illustrates the need for realistic treatment of criteria pollutant emissions inventories linked to GHG emissions policies designed for fully developed countries and states with strict existing environmental regulations. [ABSTRACT FROM AUTHOR]

Predicting air pollution events in the low atmosphere over megacities requires a thorough understanding of the tropospheric dynamics and chemical processes, involving, notably, continuous and accurate determination of the boundary layer height (BLH). Through intensive observations experimented over Beijing (China) and an exhaustive evaluation of existing algorithms applied to the BLH determination, persistent critical limitations are noticed, in particular during polluted episodes. Basically, under weak thermal convection with high aerosol loading, none of the retrieval algorithms is able to fully capture the diurnal cycle of the BLH due to insufficient vertical mixing of pollutants in the boundary layer associated with the impact of gravity waves on the tropospheric structure. Consequently, a new approach based on gravity wave theory (the cubic root gradient method: CRGM) is developed to overcome such weakness and accurately reproduce the fluctuations of the BLH under various atmospheric pollution conditions. Comprehensive evaluation of CRGM highlights its high performance in determining BLH from lidar. In comparison with the existing retrieval algorithms, CRGM potentially reduces related computational uncertainties and errors from BLH determination (strong increase of correlation coefficient from 0.44 to 0.91 and significant decreases of the root mean square error from 643 to 142 m). Such a newly developed technique is undoubtedly expected to contribute to improving the accuracy of air quality modeling and forecasting systems. [ABSTRACT FROM AUTHOR]

Within the framework of the Satellite-based Monitoring Initiative for Regional Air quality (SAMIRA) project, the near-real-time (NRT) operation has been documented for an in-house developed algorithm used for the retrieval of aerosol optical depth (AOD) maps from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) sensor onboard the Meteosat Second Generation (MSG). With the frequency of 15 min at a spatial resolution of roughly 5.5 × 5.5 km the AOD maps are provided for the country domains of Poland, the Czech Republic, Romania, and Southern Norway. A significant improvement has been reported in terms of modification of the existing prototype algorithm that it suits the operational NRT AOD retrieval for an extended area. This is mainly due to the application of the optimal interpolation method for the AOD estimation on reference days with the use of ground-based measurements of the Aerosol Robotic Network (AERONET) and the Aerosol Research Network (PolandAOD-NET) as well as simulations of the Copernicus Atmosphere Monitoring Service (CAMS). The main issues that have been addressed regarding surface reflectance estimation, cloud screening and uncertainty calculation. Exemplary maps of the NRT retrieval have been presented. [ABSTRACT FROM AUTHOR]