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Traffic restriction policy (TRP) is common practice to alleviate urban congestion and air pollution. However, effects of TRP for non-local vehicles and elevated expressways on local and regional air quality are understudied. Using air pollutant data collected by ground-based national and roadside environmental monitoring stations and field measurements, comparative research design and regression discontinuity analysis were performed to investigate the local and regional variations in air pollutant concentrations with and without the TRP. Results indicate that traffic restriction significantly decreased the local and regional concentration levels of primary gaseous pollutants (e.g., NOX and CO), particularly under more stringent TRP during China International Import Expo, which also brought an ozone increase due to the reduced effect of NOX titration. However, the TRP only exhibited limited impacts on mitigating regional particulate pollution. These findings suggest that the air quality improvements due to the non-local TRP highly depend on the types of pollutants and the spatial locations.

This study investigated the temporal variations of air pollutant concentrations in Ogoni area, Niger Delta, Nigeria. The study used hourly data measured over 8 hours for 12 months at selected locations within the area. The analyses were based on time series and time variations techniques in Openair packages of R programming software. The variations of air pollutant concentrations by time of day and days of week were simulated. Hours of the day, days of the week and monthly variations were graphically simulated. Variations in the mean concentrations of air pollutants by time were determined at 95 % confidence intervals. Sulphur dioxide (SO2), Nitrogen dioxide (NO2), ground level Ozone (O3) and fine particulate matter (PM2.5) concentrations exceeded permissible standards. Air pollutant concentrations showed increase in January, February, November and December compared to other months. Simulation showed that air pollutants varied significantly by hours-of-the-day and days-of-the-week and months-of-the-year. Analysis of temporal variability revealed that air pollutant concentrations increased during weekdays and decreased during weekends. The temporal variability of air pollutants in Ogoni area showed that anthropogenic activities were the main sources of air pollution in the area, therefore further studies are required to determine air pollutant dispersion pattern and evaluation the potential sources of air pollution in the area.

An integrated approach was used to estimate the number of COVID-19 patients related to air quality and meteorological phenomena. Additionally, the air quality during pre-lockdown, lockdown, and post-lockdown stages of the COVID-19 pandemic was assessed to determine the effect of the infection containment measures taken in Bangladesh during the pandemic. The air quality was assessed based on measurements of nitrogen dioxide (NO2), sulfur dioxide (SO2), ozone (O3), carbon monoxide (CO), black carbon, particulate matter (PM2.5 and PM10), and aerosol optical depth. Time-averaged maps of these parameters have been generated from NASA’s (National Aeronautics and Space Administration) website. Values of these parameters have also been collected from a continuous air monitoring station (CAMS) located in Bangladesh’s north-western city Rajshahi. The comparison shows that lockdown during the pandemic has brought significant improvements in air quality. However, the improvement was not sustained, since rapid increases in the air pollutant concentrations were observed in the post-lockdown period. Furthermore, Pearson correlation coefficients between each air quality variable and the daily new COVID-19 case rates were calculated. Different meteorological variables during the same time periods were determined to observe the variation in Rajshahi city. Relationships of these variables with the case rates were also established. Additionally, statistical analyses of the obtained data have been conducted for the measured variables using the Kruskal–Wallis test to assess the differences in the observed data among the pre-lockdown, lockdown, and post-lockdown periods. Dunn’s “Q” test was employed to test if the variables showed significance statistical difference during the Kruskal–Wallis test for pairwise comparisons. From the study, it has been observed that both meteorological variables and air quality parameters have significant relationship with daily new COVID-19 case rates. Both positive and negative associations of these parameters with the COVID-19 case rates have been observed. Very high air pollution has been observed in the post-lockdown period. Thus, it is recommended that appropriate authorities undertake corrective measures to protect the environment in cities with large populations. This study provides guidance for decision makers and health officials for future research and potentially reducing the spread of COVID-19.

Graphical abstract This paper reviews the progress and effectiveness of Programs to Control Vehicle Emissions (PCVEs), comparing the experiences in the United States (US), European Union (EU), China, and Brazil. We present a timeline comparison of updates and differences in standards for light-duty vehicle (LDV) compliance. We then review the benefits of controlling LDV emissions on air quality, derived from previous relevant studies. Emission standards have been increasingly restricted in all evaluated PCVEs. However, some technical aspects such as dynamometer test cycles, re-testing structure of environmental protection agency, homogeneity of new and in-use vehicles inspection and maintenance, on-board diagnostics requirements are more consolidated in the US. Previous studies at different scales show the success of PCVEs in reducing vehicle emissions and air pollutant concentrations in the US, EU, China, and Brazil. Despite PCVEs has been achieving relative success, vehicular emissions are still a major threat to air quality around the world, especially in developing countries or ascending economies whose fleet grows dramatically. In places where the air quality standards recommended by the World Health Organization (WHO) are violated, it would be required the implementation of more stringent regulations with a well-designed, and homogeneous compliance policy over regional and national territories. This work contributes to clarifying the current challenges and successful experiences on regulating vehicular emissions worldwide. Supplementary Information The online version contains supplementary material available at 10.1007/s10098-021-02238-1.

Air quality directly relates to human health and economic and social sustainable development. In this study, we aim to explore the characteristics of the air pollutant in urban forest areas (UFA) and the relationship of the air pollutant between UFA and other urban areas (OUA). As one of the major vegetated spaces in urban area, UFA are places that can provide better air quality to the public. To investigate the characteristics of the ambient air pollutants in UFA, six monitoring stations in Fuzhou collected six criteria of air pollutant data (PM2.5, PM10, CO, SO2, NO2 and O3) between May 1st, 2014 and April 30th, 2017. To reveal the characteristics of the air quality in the UFA and compare the difference between UFA and OUA, variations of air pollutant concentrations, attainment rates, major air pollutants (MAP), and the air pollutant source were analyzed. Results showed that compared to the most cities in China, the six criteria air pollutants concentrations were relatively low in Fuzhou; the annual average attainment rate in UFA was higher than in OUA; in all seasons, except winter, PM10 was the most frequent MAP in UFA, followed by PM2.5 and O3, and the correlations of the MAP between UFA and OUA, indicated that the source of air pollution came from the urban areas of Fuzhou city. Monitoring and analyzing the variations of the ambient air quality in UFA are therefore of considerable significance to the public, as well as to green space managers, designers and urban policymakers.

Air pollutant concentrations in urban areas exhibit strong spatial and temporal trends because of variations in land use and source distributions. While traffic is an important urban source of pollutants such as NO2 and particulate matter (PM), other sources can also contribute to observed spatiotemporal patterns. PM exposures are further complicated because urban-scale variations in fine particulate matter mass (PM2.5) concentrations can be decoupled from variations in PM physicochemical properties such as composition and the concentration of ultrafine particles (UFPs). Our goal was to quantify spatial gradients in both pollutant concentrations and PM characteristics over spatial scales ranging from hundreds of meters to several kilometers, along with sources influencing the observed spatial patterns. Continuous air pollutant concentrations were measured with a distributed network that consisted of a low-cost sensor package to measure pollutant gases (CO, NO2, O3, CO2), optical measurements of particulate black carbon (BC) and PM2.5, and UFP using condensation particle counters. The sampling sites were classified according to nearby land use for traffic, population, and restaurant density. Mean concentrations of BC and UFP vary by factors of approximately 4 and 3, respectively, across five typical urban sites in a 16 km2 sampling domain. Differences in traffic volume and composition (e.g., diesel bus activity) can describe some, but not all of the observed spatial and temporal differences between sites. Specifically, two sites separated by ~ 500 m in a downtown central business district have significant differences in BC (factor of 2) and UFP (~ 30%), though similar PM2.5 (

Air pollution harms human health and the environment. Several regulatory efforts and different actions have been taken in the last decades by authorities. Air quality trend analysis represents a valid tool in assessing the impact of these actions taken both at national and local levels. This paper presents for the first time the capability of the Italian national chemical transport model, AMS-MINNI, in capturing the observed concentration trends of three air pollutants – NO2, inhalable particles having diameter less than 10 µm (PM10), and O3 – in Italy over the period 2003–2010. We firstly analyse the model performance finding it in line with the state of the art of regional air quality modelling. The modelled trends result in a general significant downward trend for the three pollutants and, in comparison with observations, the values of the simulated trends were of a similar magnitude for NO2 (in the range −3.0 to −0.5 µg m−3 yr−1), while a smaller range of trends was found than those observed for PM10 (−1.5 to −0.5 µg m−3 yr−1) and O3 maximum daily 8 h average concentration (−2.0 to −0.5 µg m−3 yr−1). As a general result, we find good agreement between modelled and observed trends; moreover, the model provides a greater spatial coverage and statistical significance of pollutant concentration trends with respect to observations, in particular for NO2. We also conduct a qualitative attempt to correlate the temporal concentration trends to meteorological and emission variability. Since no clear tendency in yearly meteorological anomalies (temperature, precipitation, geopotential height) was observed for the period investigated, we focus the discussion of concentration trends on emission variations. We point out that, due to the complex links between precursor emissions and air pollutant concentrations, emission reductions do not always result in a corresponding decrease in atmospheric concentrations, especially for those pollutants that are formed in the atmosphere such as O3 and the major fraction of PM10. These complex phenomena are still uncertain and their understanding is of the utmost importance in planning future policies for reducing air pollution and its impacts on health and ecosystems.

Excessive exposure schoolchildren to air pollution can lead to long-lasting health problems, allergies and respiratory disease. It is well known that the major factors contributing to increase of air pollution are motor vehicles and industries. Thus, it is important to analyze the spatial temporal air pollutant concentrations and its relation with school location as the location of schools and its surrounding can increase their exposure. In this study, six schools in Johor were selected and the land use surrounding the schools were updated using ArcGIS. The Inverse Distance Weighting (IDW) interpolation technique was used to identify which schools’ area in Johor has a higher range of air pollutant concentration. There are four air pollution parameters obtained from the Department of Environment (DOE) which are PM2.5, CO, O3 and SO2. Hourly air pollutant concentration reading was obtained from the DOE in order to analyze air pollutant concentration during school period. The results obtained from the IDW technique showed that Sekolah Menengah Pasir Gudang (2) located in Pasir Gudang, Malaysia has reached a very unhealthy and hazardous level as compared to other schools in Johor. On the other hand, Sekolah Menengah Kebangsaan Tanjung Pengelih, Pengerang, Malaysia showed good to unhealthy range as compared to other schools in Johor. The spatial autocorrelation tool was used to analyze the relationship between the air pollution concentration and the school’s location in Johor. The results showed that the Moran’s Indices is positive showing a strong relationship that is clustering. It can be stated that there is a relationship between air pollutant concentrations with the school locations.

STUDY QUESTION To what extent are ambient concentrations of particulate matter SUMMARY ANSWER In this large prospective cohort study of Black women, ambient concentrations of O3, but not PM2.5 or NO2, were associated with increased risk of UL. WHAT IS KNOWN ALREADY UL are benign tumors of the myometrium that are the leading cause of gynecologic inpatient care among reproductive-aged women. Black women are clinically diagnosed at two to three times the rate of white women and tend to exhibit earlier onset and more severe disease. Two epidemiologic studies have found positive associations between air pollution exposure and UL risk, but neither included large numbers of Black women. STUDY DESIGN, SIZE, DURATION We conducted a prospective cohort study of 21 998 premenopausal Black women residing in 56 US metropolitan areas from 1997 to 2011. PARTICIPANTS/MATERIAL, SETTING, METHODS Women reported incident UL diagnosis and method of confirmation (i.e. ultrasound, surgery) on biennial follow-up questionnaires. We modeled annual residential concentrations of PM2.5, NO2 and O3 throughout the study period. We used Cox proportional hazards regression models to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for a one-interquartile range (IQR) increase in air pollutant concentrations, adjusting for confounders and co-pollutants. MAIN RESULTS AND THE ROLE OF CHANCE During 196 685 person-years of follow-up, 6238 participants (28.4%) reported physician-diagnosed UL confirmed by ultrasound or surgery. Although concentrations of PM2.5 and NO2 were not appreciably associated with UL (HRs for a one-IQR increase: 1.01 (95% CI: 0.93, 1.10) and 1.05 (95% CI: 0.95, 1.16), respectively), O3 concentrations were associated with increased UL risk (HR for a one-IQR increase: 1.19, 95% CI: 1.07, 1.32). The association was stronger among women age LIMITATIONS, REASONS FOR CAUTION Our measurement of air pollution is subject to misclassification, as monitoring data are not equally spatially distributed and we did not account for time-activity patterns. Our outcome measure was based on self-report of a physician diagnosis, likely resulting in under-ascertainment of UL. Although we controlled for several individual- and neighborhood-level confounding variables, residual confounding remains a possibility. WIDER IMPLICATIONS OF THE FINDINGS Inequitable burden of air pollution exposure has important implications for racial health disparities, and may be related to disparities in UL. Our results emphasize the need for additional research focused on environmental causes of UL. STUDY FUNDING/COMPETING INTEREST(S) This research was funded by the National Cancer Institute (U01-CAA164974) and the National Institute of Environmental Health Sciences (R01-ES019573). L.A.W. is a fibroid consultant for AbbVie, Inc. and accepts in-kind donations from Swiss Precision Diagnostics, Sandstone Diagnostics, FertilityFriend.com and Kindara.com for primary data collection in Pregnancy Study Online (PRESTO). M.J. declares consultancy fees from the Health Effects Institute (as a member of the review committee). The remaining authors declare they have no actual or potential competing financial interests. TRIAL REGISTRATION NUMBER N/A

Air pollution has been an environmental problem exerting serious impact on human health. An accurate prediction of air pollutant concentrations in space and time is essential to the mitigation and minimization of exposure to air pollution, particularly in cities. To take advantage of data collected at monitoring stations and the effect of secondary information such as meteorological factors, this paper proposes a flexible hierarchical Bayesian model (HBM) which can predict air pollutant concentrations in space and time. Within the framework, spatial temporal Kriging (STK) is employed for interpolation and the obtained results are used as priors. Because of the undesirable performance of the STK, the likelihood in the form of a nonlinear regression (NLR) with skewed normal residuals is employed to take into account the effect of meteorological factors to derive the posterior distribution of air pollutant concentrations. Due to high dimensionality and complexity, the formulation of the posterior distribution is non-analytic. We thus need to draw samples from the estimated parameters of the posterior distribution with Markov chain Monte Carlo (MCMC) method, and approximate the population characteristics with the sample characteristics. We evaluate the HBM with the concentrations of air pollutants and the meteorological variables from Northern China. For all pollutants, in the cross-validation (CV) experiments, the HBM reduces the root mean square error (RMSE) of NLR and STK by at least 0.08 and 0.4, and $$R^{2}$$ by at least 0.02 and 0.4. The empirical results show that the proposed HBM generally outperforms the NLR and STK in terms of efficiency, accuracy and robustness. The proposed framework is general enough for the analysis of spatial-temporal data of all kinds.

Background Although the associations of outdoor air pollution exposure with mortality and hospital admissions are well established, few previous studies have reported on primary care clinical and prescribing data. We assessed the associations of short and long-term pollutant exposures with General Practitioner respiratory consultations and inhaler prescriptions. Methods Daily primary care data, for 2009–2013, were obtained from Lambeth DataNet (LDN), an anonymised dataset containing coded data from all patients (1.2 million) registered at general practices in Lambeth, an inner-city south London borough. Counts of respiratory consultations and inhaler prescriptions by day and Lower Super Output Area (LSOA) of residence were constructed. We developed models for predicting daily PM2.5, PM10, NO2 and O3 per LSOA. We used spatio-temporal mixed effects zero inflated negative binomial models to investigate the simultaneous short- and long-term effects of exposure to pollutants on the number of events. Results The mean concentrations of NO2, PM10, PM2.5 and O3 over the study period were 50.7, 21.2, 15.6, and 49.9 μg/m3 respectively, with all pollutants except NO2 having much larger temporal rather than spatial variability. Following short-term exposure increases to PM10, NO2 and PM2.5 the number of consultations and inhaler prescriptions were found to increase, especially for PM10 exposure in children which was associated with increases in daily respiratory consultations of 3.4% and inhaler prescriptions of 0.8%, per PM10 interquartile range (IQR) increase. Associations further increased after adjustment for weekly average exposures, rising to 6.1 and 1.2%, respectively, for weekly average PM10 exposure. In contrast, a short-term increase in O3 exposure was associated with decreased number of respiratory consultations. No association was found between long-term exposures to PM10, PM2.5 and NO2 and number of respiratory consultations. Long-term exposure to NO2 was associated with an increase (8%) in preventer inhaler prescriptions only. Conclusions We found increases in the daily number of GP respiratory consultations and inhaler prescriptions following short-term increases in exposure to NO2, PM10 and PM2.5. These associations are more pronounced in children and persist for at least a week. The association with long term exposure to NO2 and preventer inhaler prescriptions indicates likely increased chronic respiratory morbidity.

Estimates of daily air pollution concentrations with complete spatial and temporal coverage are important for supporting epidemiologic studies and health impact assessments. While numerous approaches have been developed for modeling air pollution, they typically only consider each pollutant separately. We describe a spatial multipollutant data fusion model that combines monitoring measurements and chemical transport model simulations that leverages dependence between pollutants to improve spatial prediction. For the contiguous United States, we created a data product of daily concentration for 12 pollutants (CO, NOx, NO(2), SO(2), O(3), PM(10), and PM(2.5) species EC, OC, NO(3), NH(4), SO(4)) during the period 2005 to 2014. Out-of-sample prediction showed good performance, particularly for daily PM(2.5) species EC (R(2) = 0.64), OC (R(2) = 0.75), NH(4) (R(2) = 0.84), NO(3) (R2 = 0.73), and SO(4) (R(2) = 0.80). By employing the integrated nested Laplace approximation (INLA) for Bayesian inference, our approach also provides model-based prediction error estimates. The daily data product at 12km spatial resolution will be publicly available immediately upon publication. To our knowledge this is the first publicly available data product for major PM(2.5) species and several gases at this spatial and temporal resolution.

Environmental finance has gradually become an important tool for solving atmospheric pollution problems. The creation of options based on an atmospheric environmental health index (AEHI) is designed to support a rational pricing of financial products related to the health of the atmospheric environment. To begin with, the improved Ornstein-Uhlenbeck model is established to predict the change in air pollutant concentrations, and then we construct the AEHI by using exposure-response Poisson regression model. In addition, an options pricing method for AEHI is proposed based on Esscher transform theory, and the universal investment strategy for enterprises is formulated to hedge operational risk by using the AEHI option. Finally, we use Beijing city of China, as a case study to demonstrate how to determine the AEHI options prices for two diseases in three age groups, and propose the investment strategy for an insurance enterprise. The AEHI options should therefore be used to compensate for the damage to human health caused by air pollution, and to hedge against the operational risks of related industries.

New challenges are emerging in fine-scale air quality modeling in China due to a lack of high-resolution emission maps. Currently, only a few emission sources have accurate geographic locations (point sources), while a large part of sources, including industrial plants, are estimated as provincial totals (area sources) and spatially disaggregated onto grid cells based on proxies; this approach is reasonable to some extent but is highly questionable at fine spatial resolutions. Here, we compile a new comprehensive point source database that includes nearly 100,000 industrial facilities in China. We couple it with the frame of Multi-resolution Emission Inventory for China (MEIC), estimate point source emissions, combine point and area sources, and finally map China’s anthropogenic emissions of 2013 at the spatial resolution of 30″×30″ (~1 km). Consequently, the percentages of point source emissions in the total emissions increase from less than 30% in the MEIC up to a maximum of 84% for SO2 in 2013. The new point source-based emission maps show the uncoupled distribution of emissions and populations in space at fine spatial scales, however, such a pattern cannot be reproduced by any spatial proxy used in the conventional emissions mapping. This new accurate high-resolution emission mapping approach reduces the modeled biases of air pollutant concentrations in the densely populated areas compared to the raw MEIC inventory, thus improving the assessment of population exposure.

The relationship between fine particulate matter (PM2.5) and respiratory disease outcomes among children aged 0 to 14 years in Lanzhou, China, was evaluated. We utilized a generalized additive model linked by a quasi-Poisson distribution to examine the associations between PM2.5 and paediatric respiratory outpatient visits for time lags of 0 up to 7 days, and stratified by gender, age, and season. Cases of respiratory disease in children were collected from 3 large hospitals for the years 2014–2017 and then linked with air pollutant concentrations from 4 air quality monitoring stations by date. We observed positive and significant associations between PM2.5 and respiratory disease from the lag to lag 7, and from lag01 to lag07, with ER reaching the maximum value at lag07. For each 10 μg/m3 increase in PM2.5 (lag07), the associated increment in respiratory diseases was 2.83% (95% CI 1.80%–3.86%). Males were more sensitive to the adverse effects, and the association was more significant in spring (from March to May) and winter (from December to the next February). Overall, the child group (age 3–6 years) demonstrated a higher risk of respiratory disease after PM2.5 exposure. The associations between ambient PM2.5 and respiratory hospital outpatients among young children became partially attenuated after the adjustment for gaseous pollutants in subgroups. The exposure-response curves were positive and generally nonlinear but flatted at concentrations over 60 μg/m3. This research found a significant association between ambient PM2.5 levels and hospital outpatient visits in child with respiratory diseases in Lanzhou, China.

In order to control the spread of COVID-19, China had implemented strict lockdown measures. The closure of cities had had a huge impact on human production and consumption activities, which had greatly reduced population mobility. This article used air pollutant data from 341 cities in mainland China and divided these cities into seven major regions based on geographic conditions and climatic environment. The impact of urban blockade on air quality during COVID-19 was studied from the perspectives of time, space, and season. In addition, this article used Normalized Difference Vegetation Index (NDVI) to systematically analyze the characteristics of air pollution in the country and used the Pearson correlation coefficient to explore the relationship between NDVI and the air pollutant concentrations during the COVID-19 period. Then, linear regression was used to find the quantitative relationship between NDVI and AQI, and the fitting effect of the model was found to be significant through t test. Finally, some countermeasures were proposed based on the analysis results, and suggestions were provided for improving air quality. This paper has drawn the following conclusions: (1) the concentration of pollutants varied greatly in different regions, and the causes of their pollution sources were also different. The region with the largest decline in AQI was the Northeast China (60.01%), while the AQI in the southwest China had the smallest change range, and its value had increased by 1.72%. In addition, after the implementation of the city blockade, the concentration of NO2 in different regions dropped the most, but the increase in O3 was more obvious. (2) Higher vegetation coverage would have a beneficial impact on the atmospheric environment. Areas with higher NDVI values have relatively low AQI. There is a negative correlation between NDVI and AQI, and an average increase of 0.1 in NDVI will reduce AQI by 3.75 (95% confidence interval). In the case of less human intervention, the higher the vegetation coverage, the lower the local pollutant concentration will be. Therefore, the degree of vegetation coverage would have a direct or indirect impact on air pollution.

Air quality has been a major health issue in urban areas in recent decades. Human activities release a large number of pollutants into the atmosphere which has a direct impact on plant health and leads to ecosystem degradation. The objective of this study is to contribute to a better evaluation of the impact of the air quality of the city of Togo on biological resources. The determination of pollutants was done on samples of plant species with a strong link with the source of pollution. The determination of Sulfur dioxide (SO2) was done by the ripper method. The determination of carbon and estimation of CO2 and CO by the colorimetric method. The determination of nitrogen was done by the Kjeldhal method. The results showed that at the industrial level the amount of CO2 in Alternanthera repens is high with a value of 53.3911 mg/ml. On the other hand, the quantity of CO in Senna occidentalis is 44.3619 mg/ml. In Pithecellobium dulce, the quantity of SO2 and NO2 are evaluated respectively to 0.1588 mg/ml and 0.3696 mg/ml. Regarding to the dumps, the quantity of CO2 in Newbouldia laevis is very high with a value of 65.8508 mg/ml. On the other hand the amount of CO in Senna occidentalis is 51.6106 mg/ml. The quantity of SO2 in Newbouldia laevis is 0.2101 mg/ml and NO2 in Ocimum canum is 0.2744 mg/ml. At the level of roads, the quantities of CO2 and CO in Eragrostis tenella are very high with values respectively equal to 74.4092 mg/ml and 62.2654 mg/ml. On the other hand, the amount of NO2 in Amaranthus sp is 0.2304 mg/ml and that of SO2 in Eragrostis Tenella is 0.1691 mg/ml. The use of a plant bioindicator sensitive to pollutants, allowed concluding that the air of the city of Lome is polluted. The concentration of carbon dioxide and carbon monoxide is much more evident in return when the health of plant species is threatened.

Vertical observations of ozone (O3) and fine aerosol particles (PM2.5) below 3000 m above ground level were made by an O3 lidar and two hexacopter unmanned aerial system (UAS) platforms to investigate vertical distributions of the boundary-layer O3 and PM2.5 and their effects on surface air quality. Field observations were conducted in Shanghai, China before, during, and after the G20 Summit (Hangzhou) from 23 August to 13 September 2016, when stringent emission control measures were implemented over the Yangtze River Delta region. The results show that surface O3 and PM2.5 concentrations increased evidently during the emission control period due to the significant contribution of transport. Different transport types including regional transport, recirculation transport, and long-range transport were captured in the three stages of the emission control period. Regional transport mainly occurred in the middle and upper boundary layer, leading to positive altitude dependence of the boundary-layer O3 and PM2.5. Recirculation transport mainly occurred in the lower boundary layer, leading to negative altitude dependence of the boundary-layer O3 and PM2.5. Long-range transport of O3 mainly occurred in the lower free troposphere, leading to positive altitude dependence of the boundary-layer O3. Under the influence of northeasterly winds, Shanghai exhibited considerable transport potential of pollutants to Hangzhou during the emission control period. The findings in this study could help to understand complex interactions between different transport types and variations in surface air pollutant concentrations, as well as provide important implications in making regional emission control measures in the future.

Urbanization affects the quality of the air, which has drastically degraded in the past decades Air quality level is determined by measures of several air pollutant concentrations To create awareness among people, an automation system that forecasts the quality is needed The COVID-19 pandemic and the restrictions it has imposed on anthropogenic activities have resulted in a drop in air pollution in various cities in India The overall air quality index (AQI) at any particular time is given as the maximum band for any pollutant PM2 5 is a fine particulate matter of a size less than 2 5 micrometers, the inhalation of which causes adverse effects in people suffering from acute respiratory syndrome and other cardiovascular diseases PM2 5 is a crucial factor in deciding the overall AQI The proposed forecasting model is designed to predict the annual PM2 5 and AQI The forecasting models are designed using Seasonal Autoregressive Integrated Moving Average and Facebook’s Prophet Library through optimal hyperparameters for better prediction An AQI category classification model is also presented using classical machine learning techniques The experimental results confirm the substantial improvement in air quality and greater reduction in PM2 5 due to the lockdown imposed during the COVID-19 crisis

Air quality is a complex issue which depends mutually on source emission, land topography, meteorological parameters and used mathematical tools for forecasting its dispersion. One of major toxic gas is ozone which could be dangerous to human health. The present work has been developed to forecast ozone concentrations in the city of Agadir using a Recurrent Neural Network (RNN). Predicting ozone concentrations will provide useful information, especially, for decision makers in order to prevent and reduce the ozone human health impacts. The data was collected in the most polluted area in the city using a mobile monitoring station during a period of 60 days. We have tested different neural network architectures and we found that the 1-hour forecast model, whose input parameters are a combination of meteorological parameters as well as CO and SO\(_2\), give the most optimal results. Coefficient of Correlation (CC), Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) were used to evaluate the statistical agreement between observed and predicted values. The model successfully predicts the ozone concentration by a bias of 4 \(\mu\)g.m\(^{-3}\) over 24 hours and which a correlation coefficient is more than 80%. This work highlights the ability of the recurrent neural networks to forecast air pollutant concentrations in urban areas.

In parallel with rapidly increasing population and number of motor vehicles, irregular urbanization, and unplanned industrialization, air pollution has reached dangerous levels in developing cities. Various industries such as textile, automotive, chemical, rubber and plastic industries are located in Bursa. In addition, the region receives a lot of migration and there is an intensive air pollution problem due to dense urbanization. The air quality monitoring station results showed that the PM10 and NOx are the main pollutants reducing air quality in the city. Despite the much efforts and regulations, air quality level has been getting worst year by year. Stakeholders were brought together to explore the true causes of non-blocking emissions, identify resource loads and priorities, and develop solutions. The current level and variation of air pollutant concentrations depending on years were presented to stakeholders. A survey and discussion were performed within the workshop, and consequently; industry, transportation, heating and uncontrolled combustion activities came front. Especially the fact that the industry is located in the city and the transportation network of the city is inadequate has emerged as the main source of air pollution problem. In order to develop effective solution, it was emphasized that effective supervision should come to the forefront and new industrial facilities should not be established in the regions in or near the city.

To study the wet removal effect of rainfall on the air pollutants in Sichuan Basin, the WRF-Chem model is adopted to simulate the changes of air pollutant concentrations before and after a rainfall episode. The results show that continuous rainfall with small-magnitude rainfall (0.3–0.5 mm h−1) has an obvious effect in reducing the concentrations of PM2.5, PM10, SO2 and NO2 except for O3. There is an upper limit of the impact of continuous rainfall on air pollutants. The reduction of concentrations of particulate pollutants at the level of 300–2500 m is due to its participation in the nucleation process. When rainfall occurs and vertical diffusion is not restricted, the concentration of pollutants on the ground are reduced more quickly by being transported to high altitude. In urban areas with near-surface inversion, there is an anti-correlation between concentrations and wind speed. The correlation coefficients for the concentrations of PM2.5, PM10, SO2 and NO2 with the wind speed are −0.85, −0.86, −0.77 and −0.82, respectively. In suburban stations with the small initial concentrations, the decrease of concentrations is mainly due to the increase of rainfall intensity, but has little relationship with the height of PBL and the wind speed. The small gradient of sea level pressure (less than 1 hPa) and the near-surface inversion in the Chengdu Plain are conducive to the accumulation of air pollutants.

Aim/Objective: This study aims to identify the key trends among different types of LSTM networks and their performance and usage for air pollutants(PM2:5 and PM10) concentrations prediction. Methods: In this study, the extensive research efforts were made for Particulate Matters (i.e., PM10 and PM2:5) prediction using several LSTM networks, namely Vanilla, Stacked, and Bidirectional. These are trained and tested using air quality data, retrieved from the Central Pollution Control Board (CPCB) of the town Bawana, Delhi. Realtime hourly a data from 2018 to 2020 with nine air pollutants are considered for experimental analysis. We conducted data preparation strategy to select the best features, which improve the quality of the data. An adequate number of experiments are conducted to choose the best hyperparameters using Python package TensorFlow. Findings: MSE, MAE, RMSE, and R2 parameters are used as the statistical criteria for evaluating the model’s performances.The numerical experiments revealed that deep neural networks could predict the Particulate Matters (mg/m3) with high accuracy. We found that Stacked LSTM with minimum MSE, MAE, RMSE, and maximum R2 works better than the other two methods, i.e., Vanilla LSTM and Bidirectional LSTM for PM2:5 and PM10 concentrations prediction. The empirical, experimental analysis also shows that Vanilla, Stacked, and Bidirectional LSTM models have comparatively minimum MSE, MAE, RMSE, and maximum R2 for PM2:5 than PM10 concentration prediction. Applications: With the help of a predictive model, one can find reliable fine concentration prediction information for a particular area. The resultant information on relative performance can help researchers in the selection of an appropriate LSTM algorithm for their studies. Keywords: Air pollutants; air quality index; PM concentrations; LSTM;TensorFlow; air pollution

Background The association between long-term exposure to air pollutants, including nitrogen dioxide (NO2), carbon monoxide (CO), sulfur dioxide (SO2), ozone (O3), and particulate matter 10 μm or less in diameter (PM10), and mortality by ischemic heart disease (IHD), cerebrovascular disease (CVD), pneumonia (PN), and chronic lower respiratory disease (CLRD) is unclear. We investigated whether living in an administrative district with heavy air pollution is associated with an increased risk of mortality by the diseases through an ecological study using South Korean administrative data over 19 years. Methods A total of 249 Si-Gun-Gus, unit of administrative districts in South Korea were studied. In each district, the daily concentrations of CO, SO2, NO2, O3, and PM10 were averaged over 19 years (2001–2018). Age-adjusted mortality rates by IHD, CVD, PN and CLRD for each district were averaged for the same study period. Multivariate beta-regression analysis was performed to estimate the associations between air pollutant concentrations and mortality rates, after adjusting for confounding factors including altitude, population density, higher education rate, smoking rate, obesity rate, and gross regional domestic product per capita. Associations were also estimated for two subgrouping schema: Capital and non-Capital areas (77:172 districts) and urban and rural areas (168:81 districts). Results For IHD, higher SO2 concentrations were significantly associated with a higher mortality rate, whereas other air pollutants had null associations. For CVD, SO2 and PM10 concentrations were significantly associated with a higher mortality rate. For PN, O3 concentrations had significant positive associations with a higher mortality rate, while SO2, NO2, and PM10 concentrations had significant negative associations. For CLRD, O3 concentrations were associated with an increased mortality rate, while CO, NO2, and PM10 concentrations had negative associations. In the subgroup analysis, positive associations between SO2 concentrations and IHD mortality were consistently observed in all subgroups, while other pollutant-disease pairs showed null, or mixed associations. Conclusion Long-term exposure to high SO2 concentration was significantly and consistently associated with a high mortality rate nationwide and in Capital and non-Capital areas, and in urban and rural areas. Associations between other air pollutants and disease-related mortalities need to be investigated in further studies.

ObjectiveAmbient air pollution is associated with ischaemic stroke incidence. However, most of the previous studies used stroke-related hospital admission rather than stroke onset itself. This study aimed to evaluate the relationship between ambient air pollutant exposures and acute ischaemic stroke based on the timing of symptom onset.MethodsA time-stratified, case-crossover analysis was performed among 520 patients who had ischaemic stroke admitted to the Second Hospital of Tianjin Medical University (Tianjin, China) between 1 April 2018 and 31 March 2019 (365 days). Daily air pollutant concentrations of particulate matter with aerodynamic diameter 2.5 µm, particulate matter with aerodynamic diameter 10 µm (PM10), sulfur dioxide, nitrogen dioxide, carbon monoxide and ozone were obtained from fixed-site monitoring stations. We used conditional logistic regression to estimate OR and 95% CI corresponding to an increase in IQR of each air pollutant after adjusting for the effects of temperature and relative humidity.ResultsOverall, a higher risk of ischaemic stroke was found between April and September. During this period PM10 was associated with an increased risk of ischaemic stroke (1-day lag: OR=1.49, 95% CI 1.09 to 2.02; 3-day mean: OR=1.58, 95% CI 1.09 to 2.29) among patients between 34 and 70 years old. Positive associations were also observed between PM10 (1-day lag: OR=1.51, 95% CI 1.10 to 2.07; 3-day mean: OR=1.57, 95% CI 1.08 to 2.29), ozone (1-day lag: OR=1.83, 95% CI 1.16 to 2.87; 3-day mean: OR=1.90, 95% CI 1.06 to 3.42) and ischaemic stroke occurrence among those with hyperlipidaemia.ConclusionOur results suggest that air pollution is associated with a higher risk of ischaemic stroke in younger people or people with hyperlipidemia. These findings still need to be further investigated.

To forecast possible future environmental risks, numerous models are developed to predict the hourly values or daily averages of air pollutant concentrations using streaming data (a kind of big data collected from the Internet). On the one hand, real-time hourly data is massive and redundant, making it difficult to process. On the other hand, daily averages cannot reflect the fluctuations of air pollutant concentrations throughout the day. Therefore, a double decomposition and optimal combination ensemble learning approach is proposed for interval-valued AQI (air quality index) forecasting in this paper. In the first decomposition, considering the strong seasonal representation of AQI, the original data of each year is decomposed into four seasonal subseries on the basis of the Chinese calendar. Subsequently, we reconstruct the data of the same season in different years to get a new seasonal series to reduce the interference of seasonal changes on AQI forecasting. In the second decomposition, due to the nonlinearity and irregularity of interval-valued AQI time series, BEMD (bivariate empirical mode decomposition) is employed to decompose the interval-valued signals into a finite number of complex-valued IMF (intrinsic mode function) components and one complex-valued residue component with different frequencies to reduce the complexity of interval times series. Interval multilayer perceptron (iMLP) is utilized to model the lower bound and the upper bound simultaneously of the total components to obtain the corresponding forecasting results, which are merged to produce the final interval-valued output by an optimal combination ensemble method. Empirical study results show that the proposed model with different datasets and different forecasting horizons is significantly better than other considered models for its superior forecasting performances.

International audience; This review analyzes the state and recent progress in the field of information support for pollen allergy sufferers. For decades, information available for the patients and allergologists consisted of pollen counts, which are vital but insufficient. New technology paves the way to substantial increase in amount and diversity of the data. This paper reviews old and newly suggested methods to predict pollen and air pollutant concentrations in the air and proposes an allergy risk concept, which combines the pollen and pollution information and transforms it into a qualitative risk index. This new index is available in an app (Mobile Airways Sentinel NetworK-air) that was developed in the frame of the European Union grant Impact of Air POLLution on sleep, Asthma and Rhinitis (a project of European Institute of Innovation and Technology-Health). On-going transformation of the pollen allergy information support is based on new technological solutions for pollen and air quality monitoring and predictions. The new information-technology and artificial-intelligence-based solutions help to convert this information into easy-to-use services for both medical practitioners and allergy sufferers.

China has suffered from increasing levels of ozone pollution in urban areas despite the implementation of various stringent emission reduction measures since 2013. In this study, we conducted numerical experiments with an up-to-date regional chemical transport model to assess the contribution of the changes in meteorological conditions and anthropogenic emissions to the summer ozone level from 2013 to 2017 in various regions of China. The model can faithfully reproduce the observed meteorological parameters and air pollutant concentrations and capture the increasing trend in the surface maximum daily 8 h average (MDA8) ozone (O3) from 2013 to 2017. The emission-control measures implemented by the government induced a decrease in MDA8 O3 levels in rural areas but an increase in urban areas. The meteorological influence on the ozone trend varied by region and by year and could be comparable to or even more significant than the impact of changes in anthropogenic emissions. Meteorological conditions can modulate the ozone concentration via direct (e.g., increasing reaction rates at higher temperatures) and indirect (e.g., increasing biogenic emissions at higher temperatures) effects. As an essential source of volatile organic compounds that contributes to ozone formation, the variation in biogenic emissions during summer varied across regions and was mainly affected by temperature. China's midlatitude areas (25 to 40∘ N) experienced a significant decrease in MDA8 O3 due to a decline in biogenic emissions, especially for the Yangtze River Delta and Sichuan Basin regions in 2014 and 2015. In contrast, in northern (north of 40∘ N) and southern (south of 25∘ N) China, higher temperatures after 2013 led to an increase in MDA8 O3 via an increase in biogenic emissions. We also assessed the individual effects of changes in temperature, specific humidity, wind field, planetary boundary layer height, clouds, and precipitation on ozone levels from 2013 to 2017. The results show that the wind field change made a significant contribution to the increase in surface ozone over many parts of China. The long-range transport of ozone and its precursors from outside the modeling domain also contributed to the increase in MDA8 O3 in China, especially on the Qinghai–Tibetan Plateau (an increase of 1 to 4 ppbv). Our study represents the most comprehensive and up-to-date analysis of the impact of changes in meteorology on ozone across China and highlights the importance of considering meteorological variations when assessing the effectiveness of emission control on changes in the ozone levels in recent years.

An assessment of trends in main air pollutant concentrations (including CO, SO2, NO2, O3, PM2.5, and Asbestos) is conducted for the years 2005–2016 for the city of Tehran, Iran. The focus has been on the contribution of anthropogenic emissions to the observed trend, particularly the role of related mitigation plans implemented in the period of interest. A significant decreasing trend (about 50%) was observed in CO and SO2 concentrations during the considered time interval, which was explained by improvement plans in fuel quality and vehicle emission standards. While a substantial elevation (more than 50%) in NO2 levels was detected over the study period, variant trends were observed during different periods that appear to be driven by multiple factors including the increase in the number of vehicles and fuel consumption, changes in residential heating fuel from heavy oil to natural gas, etc. The analysis revealed that the O3 formation in Tehran's atmosphere was converted from NOx-limited to NOx-saturated regime around the years 2012–2013. The overall analysis showed a slight decreasing trend (about 30%) in PM2.5 concentrations between the years 2011–2015; however, a rise in PM2.5 levels was observed between the years 2015–2016. Apart from the contribution of anthropogenic drivers, general drought in Iran has caused a considerable increase in natural dust in total PM2.5 mass. Finally, a decline in annual mean airborne asbestos fibers (more than 60%) was indicated by the current study during the years 2012 and 2015.

As much of the population lives in close proximity to high-traffic roads, there is the potential for health impact assessments based on regional air quality modeling to underpredict health impacts. We compare the estimated health impacts from fine particulate matter (PM2.5) using local (0.04 × 0.04 km upscaled to census block group resolution) compared to regional (12 × 12 km resolution) modeled concentrations for three locations in Connecticut: Hartford, New Haven, and Willimantic. We use concentration estimates from the Comprehensive Air Quality Model with Extensions (CAMx) regional model and a hybrid model combining CAMx with a near road model (HYCAMR) in the Environmental Benefits Mapping and Analysis Program—Community Edition (BenMAP-CE) to calculate the difference in estimated human health impacts using different resolution air quality estimates from PM2.5 exposure including mortality, emergency room visits, hospitalizations, and asthma exacerbation. This provides an estimate of the potential underprediction of health impacts resulting from not accounting for the sharp concentration gradients in near road environments in urban areas. The fine-scale estimates capture the elevated concentrations near the roadways leading to increased estimates of overall mortality and morbidity in the population. We find an increase in the estimated likelihood of emergency department visits and mortality in the urban core. We also compare the impact of model resolution on the health impact estimates for different demographic groups. Of the locations investigated, we see the largest differences between demographic groups in Willimantic, CT. Our results indicate that using regional air pollutant concentrations may lead to an underprediction of human health impacts from air pollution exposure.

Understanding the spatio-temporal characteristics of air pollutants is essential to improving air quality. One aspect is the question of whether green spaces can reduce air pollutant concentrations...

The restriction of daily and economic-related activities due to COVID-19 pandemic via lockdown order has been reported to improve air quality This study evaluated temporal and spatial variations of four major air pollutant concentrations across Malaysia before (March 4, 2020–March 17, 2020) and during the implementation of different phases of Movement Control Order (MCO) (March 18, 2020–May 12, 2020) from 65 official regulatory air quality stations Results showed that restriction in daily and economic activities has remarkably reduced the air quality in all sub-urban, urban, and industrial settings with relatively small contributions from meteorological conditions Overall, compared to before MCO, average concentrations of PM2 5, CO, and NO2 reduced by 23 1%, 21 74%, and 54 0%, respectively, while that of SO2 was constant The highest reduction of PM2 5, CO, and NO2 were observed in stations located in urban setting, where 63% stations showed significant reduction (p

City-wide air pollution assessments have typically relied on a small number of widely separated regulatory monitoring sites or land use regression (LUR) models built using time-integrated samples to assess annual average population-scale exposure. However, air pollutant concentrations often exhibit significant spatial and temporal variability depending on local sources and features of the built environment. In 2016, the Center for Air, Climate, and Energy Solutions (CACES) Air Quality Observatory was launched at Carnegie Mellon University to better understand urban spatial and temporal pollution gradients on the 8 h) above the regional background. Compared to the non-decomposed total pollutant signal, the short-lived or persistent enhancement pollutant signals, which should come from local sources, were better correlated with covariates used in LUR model construction. For example, Pearson r between total vehicle counts in a 100 m buffer and NO2 increased from 0.57 using the total pollutant signal to 0.83 using the persistent enhancement only. The findings from this study support building more accurate and higher time resolution (e.g., daily, hourly) LURs using low-cost sensors.

Background Pediatric allergic diseases are a major public health concern, and previous studies have suggested that exposure to traffic-related air pollution (TRAP) exposure is a risk factor. These studies have typically assessed TRAP exposure using traffic measures, such as distance to major roads, or by modeling air pollutant concentrations; however inconsistent associations with pediatric allergic diseases have often been found. Using road proximity and density, we previously found an association between TRAP and atopic eczema among approximately 15,000 children living in Seoul, Korea, heavily populated and highly polluted city in which traffic is a major emission source. We aimed to conduct a parallel analysis using modeled air pollution concentrations and thus examine the consistency of the association. Specifically, we examined the associations of individual-level annual-average concentrations of NO2, PM10, and PM2.5 with symptoms and diagnoses of three pediatric allergic diseases including asthma, allergic rhinitis, and atopic eczema. Methods The study population included 14,614 children from the Seoul Atopy Friendly School Project Survey in Seoul, Korea, in 2010. To assess individual exposures to TRAP among these children, we predicted annual-average concentrations of NO2, PM10, and PM2.5 at the children’s home addresses in 2010 using universal kriging and land use regression models along with regulatory air quality monitoring data and geographic characteristics. Then, we estimated odds ratios (ORs) of the three allergic diseases for interquartile increases in air pollution concentrations after adjusting for individual risk factors in mixed effects logistic regression. Results Symptoms and diagnoses of atopic eczema symptoms showed an association with NO2 (OR = 1.07, 95% confidence interval = 1.02–1.13; 1.08, 1.03–1.14) and PM10 (1.06, 1.01–1.12; 1.07, 1.01–1.13). ORs of PM2.5 were positive but not statistically significant (1.01, 0.95–1.07; 1.04, 0.98–1.10). No association was found between asthma and allergic rhinitis, although PM2.5 showed a marginal association with allergic rhinitis. Conclusions Our consistent findings regarding the association between TRAP and the prevalence of atopic eczema using traffic measures and surrogate air pollutants suggested the effect of TRAP on children’s health. Follow-up studies should elucidate the causal link, to support subsequent policy considerations and minimize adverse health effects in children.

In the past few decades, many urban areas around the world have suffered from severe air pollution and the health hazards that come with it, making gathering real-time air quality and air quality forecasting very important to take preventive and corrective measures. This paper proposes a scalable architecture to monitor and gather real-time air pollutant concentration data from various places and to use this data to forecast future air pollutant concentrations. Two sources are used to collect air quality data. The first being a wireless sensor network that gathers and sends pollutant concentrations to a server, with its sensor nodes deployed in various locations in Bengaluru city in South India. The second source is the real-time air quality data gathered and made available by the Government of India as a part of its Open Data initiative. Both sources provide average concentrations of various air pollutants on an hourly basis. Due to its proven track record of success with time-series data, a Long Short-Term Memory (LSTM) Recurrent Neural Network (RNN) model was chosen to perform the task of air quality forecasting. This paper critically analyses the performance of the model in two regions that exhibit a significant difference in temporal variations in air quality. As these variations increase, the model suffers performance degradation necessitating adaptive modelling.

Air pollution in New Delhi/NCR, India, is an important concern for the environment and health. We have researched Land-Use Regression models (LUR) for Fine Particulate Matter (PM2.5) and Black Carbon for the purpose of assessing the variable determinants for air pollutant concentrations. A wide range of fresh-emitting air emissions from major highways, highways, and freeways with high concentrations of Black Carbon, Nitrogen Oxides and Carbon Monoxide are increasingly evident. In the past, this is also evident. People who reside or spend a substantial amount of time on around 200 m of highways are more likely to live in these pollutants than individuals living longer distances from busy urban streets. Epidemiological studies investigating human health effects of PM2.5are susceptible, as they are based on data of a limited number of PM2.5 monitors in their field of research, to exposure calibration errors, which are a form of exposure bias estimates Satellite information may be used to increase spatial coverage, which may possibly increase our capacity to estimate PM2.5exposures. The population density and highway factors were statistically significant pollutant predictors; it indicates the significance of spatial variation and suggests that uncharacterized sources exist. In this paper, interdisciplinary reviews on various environmental pollutions that especially caused in India's National Capital discussed. This paper will give the guidelines for future research on impacts on environmental pollution observational studies.

While most countries have networks of stations for monitoring pollutant concentrations, they do not cover the whole territory continuously. Therefore, to be able to carry out a spatial and temporal study, the predictions for air pollution from unmeasured sites and time periods need to be used. The objective of this study is to predict the air pollutant concentrations of PM10, O3, NO2, SO2 and CO in sites throughout Catalonia (Spain) and time periods without a monitoring station. Compositional data (CoDa) studies the relative importance of pollutants. A novel feature in this article is combining CoDa with an indicator of total pollution. Predictions are then made using a combination of spatio-temporal models and the Bayesian Laplace Integrated Approach (INLA) inference method. The most relevant results obtained indicate that the log-ratio between NO2 and O3 has the highest variance and the best predictive accuracy in time and space. Total pollution levels are second in variance but have low spatial predictive accuracy. Third in variance is the low temporal accuracy found in the log-ratio between SO2 and the remaining pollutants. Globally, the combination of CoDa and the INLA method is suitable for making effective spatio-temporal predictions of air pollutants This work was partially financed by the SUPERA COVID19 Fund, from SAUN: Santander Universidades, CRUE and CSIC; by the COVID-19 Competitive Grant Program from Pfizer Global Medical Grants; by the Spanish Ministry of Science, Innovation and Universities/FEDER (grant number RTI2018–095518–B–C21); and the Government of Catalonia (grant number 2017SGR656) Open Access funding provided thanks to the CRUE-CSIC agreement with Elsevier