The nonlinear chemical processes involved in ozone production (P(O3)) have necessitated using proxy indicators to convey information about the primary dependence of P(O3) on volatile organic compounds (VOCs) or nitrogen oxides (NOx). In particular, the ratio of remotely sensed columns of formaldehyde (HCHO) to nitrogen dioxide (NO2) has been widely used for studying O3 sensitivity. Previous studies found that the errors in retrievals and the incoherent relationship between the column and the near-surface concentrations are a barrier in applying the ratio in a robust way. In addition to these obstacles, we provide calculational-observational evidence, using an ensemble of 0-D photochemical box models constrained by DC-8 aircraft measurements on an ozone event during the Korea-United States Air Quality (KORUS-AQ) campaign over Seoul, to demonstrate the chemical feedback of NO2 on the formation of HCHO is a controlling factor for the transition line between NOx-sensitive and NOx-saturated regimes. A fixed value (∼2.7) of the ratio of the chemical loss of NOx (LNOx) to the chemical loss of HO2+RO2 (LROx) perceptibly differentiates the regimes. Following this value, data points with a ratio of HCHO/NO2 less than 1 can be safely classified as NOx-saturated regime, whereas points with ratios between 1 and 4 fall into one or the other regime. We attribute this mainly to the HCHO-NO2 chemical relationship causing the transition line to occur at larger (smaller) HCHO/NO2 ratios in VOC-rich (VOC-poor) environments. We then redefine the transition line to LNOx/LROx∼2.7 that accounts for the HCHO-NO2 chemical relationship leading to HCHO = 3.7 × (NO2 – 1.14 × 1016 molec.cm-2). Although the revised formula is locally calibrated (i.e., requires for readjustment for other regions), its mathematical format removes the need for having a wide range of thresholds used in HCHO/NO2 ratios that is a result of the chemical feedback. Therefore, to be able to properly take the chemical feedback into consideration, the use of HCHO = a × (NO2 – b) formula should be preferred to the ratio in future works. We then use the Geostationary Trace gas and Aerosol Sensor Optimization (GeoTASO) airborne instrument to study O3 sensitivity in Seoul. The unprecedented spatial (250 × 250 m2) and temporal (∼every 2 h) resolutions of HCHO and NO2 observations form the sensor enhance our understanding of P(O3) in Seoul; rather than providing a crude label for the entire city, more in-depth variabilities in chemical regimes are observed that should be able to inform mitigation strategies correspondingly.

In recent years, near-surface ozone (O3) concentrations have been increasing, which aggravates O3 pollution. Due to the environmental threat it poses to human health, O3 pollution has become a hot topic among researchers. In this paper, we used a generalized additive model (GAM) to evaluate the complex nonlinear relationships between O3 concentration and the factors influencing O3 concentration from 2013 to 2017 in Lanzhou in Western China. We considered factors such as long-term trend, seasonality at a quarterly interval, and the weekend effect. The results showed that O3 concentration in Lanzhou was affected by many factors, and these influencing factors were correlated with each other. In the single-factor model, the adjusted coefficient of determination (R2) was 0.594, and the total deviance explained of O3 concentration was 62.3%. We detected significantly nonlinear relationships between O3 concentration and the influencing factors like air temperature, sunshine hours, wind speed, relative humidity, and the concentrations of NO2 and PM2.5. In particular, air temperature was the main driving factor for O3 concentration, which explained 24.2% of the variance (F = 350.84). In addition, we built a double-factor model to investigate the interactive influences of these influencing factors on O3 concentration variation. In the fitted double-factor model, R2 was 0.636 and the total deviance explained was 68.1%, both of which were higher (i.e., better performance) than that in the single-factor model. Among these studied influencing factors, the interaction between air temperature and air pollutants showed the greatest influence on O3 concentration variation. The results of this study can be used to assist local environmental authorities to take proactive measures for O3 pollution control in Lanzhou, Gansu.

Although emission models have been designed using vehicle data over driving cycles of a few minutes, they are often applied at large scale to estimate total emission (inventories). In between, there is a range of scales in use in traffic and environmental studies (road sections, sub-areas, etc.). Coupling a traffic microsimulation with COPERT emission factors at different scales reveals scaling biases. We compare network fuel consumption (FC) and nitrogen oxide (NOx) emissions resulting from emission calculations based on different spatial decompositions. The results show that for an area of Paris covering 3 km2, the differences due to the aggregation scale for emissions range from 5 to 17% depending on the pollutant, spatial partitioning and traffic conditions. These discrepancies can be reduced using a distance-weighted mean speed, which is not a scale-consistent definition of mean travel speed. They can almost be cancelled by using a correction term derived analytically in this paper, thus consistency can be guaranteed between emissions assessed at different scales. Finally, a case study shows that it is possible to evaluate FC and NOx emissions on a large-scale network from a sample of traffic data (probes), and obtain the corrective term to be applied to remove scaling bias. The most critical step is the accurate estimation of the total travel distance. The gaps were successfully reduced to a maximum of 8% in congestion for a penetration rate of about 20%.

Because of limited environmental monitoring data, the regional-scale impact of the deposition of fogwater radiologically contaminated by the Fukushima Daiichi Nuclear Power Plant (F1NPP) accident remains unclear. To redress this situation, we present an observational report of the radiocesium concentration in fogwater and its deposition in a Japanese forest during the early stages of the F1NPP accident (March 2011). The data were acquired by using a passive collector to capture fogwater above the forest canopy on a monthly basis. In addition, the radiocesium concentrations in monthly throughfall and stemflow were measured under the canopies of four tree species. The 137Cs activity concentration in fogwater during the observational period was 45.8 Bq L−1, which was twice as high as that present in bulk precipitation. The ratio of 137Cs in throughfall to that in bulk precipitation (TF/BP ratio) ranged from 1.0 to 2.5. The high TF/BP ratios may have been caused by the high radiocesium concentration in fogwater deposition. Based on this assumption, we assessed the TF/BP ratio according to the 137Cs activity concentrations of throughfall and bulk precipitation measured in various mountainous regions in East Japan. Our results reveal that the TF/BP ratio is high at some sites and that it increases with elevation. Sites with a high TF/BP ratio were almost entirely situated in areas of fogwater deposition, as predicted by an atmospheric dispersion model. In addition, sites with a high TF/BP ratio were above the cloud base at the time when plumes with high atmospheric 137Cs activity concentrations passed through the areas. Thus, these measurements of radiocesium in fogwater during the early stages of the F1NPP accident provide evidence that fogwater with high radioactive contamination was deposited in the forested mountain areas of East Japan. Given the major impact of fogwater deposition of radiocesium, its role should be considered carefully to better understand radiocesium cycling in forest ecosystems.

The Community Multiscale Air Quality (CMAQ) model with a source-oriented SAPRC-11 photochemical mechanism is developed in this study to quantify the source region contributions to surface O3 in Beijing and Shanghai in August 2013. Non-background O3 attributed to NOx (O3_NOx) and VOCs (O3_VOC) emitted from different source regions are determined using a three-regime approach that correctly attributes O3 to NOx and VOC precursors throughout the entire range of NOx-VOC-O3 formation sensitivity. Averaged over the entire month and all grid cells, local emissions (51%) and emissions from Hebei (31%) are the two major contributors to non-background daily maximum 8-h (DM8H) O3 in Beijing. In Shanghai, local, Zhejiang and Jiangsu emissions account for 53%, 19% and 14% of the non-background DM8H O3. Significant variations in local emission contributions are predicted among different model grid cells for both cities (Beijing, 6–80%; Shanghai, 3–76%). On high O3 days in Beijing, the wind is persistently from the south with high wind speed (∼5 m s−1) in the evening and night. This leads to significant regional contributions of O3 from Hebei, along with regional transport of VOCs and NOx. In Shanghai, high O3 days are associated with southwesterly/westerly wind in the morning, rotating to southeast in the early afternoon in a counter-clockwise direction. The surface wind then gradually turns back to southwest in the afternoon until the next morning, along with reduced wind speed. In Shanghai, daytime O3 at the urban center is almost entirely due to local emissions. Low wind speed in the evening and night time allows local NOx emissions to efficiently titrate regional O3. In both cities, NOx emissions are not transported regionally as efficiently as VOCs. Source region contribution analysis of the concentration weighted maximum incremental reactivity (CWMIR) shows that VOCs from other regions are less reactive than locally emitted VOCs. HCHO and acetaldehyde (CCHO) generated from the oxidation of other VOCs are important contributors to regionally transported reactive VOCs. In both regions, the overall CWMIR in both cities is quite similar (∼4 mol O3 per mole of VOC).

Dust storms are believed to play an essential role in many climatological, geochemical, and environmental processes. This atmospheric phenomenon can have a significant negative impact on public health and significantly disturb natural ecosystems. Identifying dust-source areas is thus a fundamental task to control the effects of this hazard. This study is the first attempt to identify dust source areas using hybridized machine-learning algorithms. Each hybridized model, designed as an intelligent system, consists of an adaptive neuro-fuzzy inference system (ANFIS), integrated with a combination of metaheuristic optimization algorithms: the bat algorithm (BA), cultural algorithm (CA), and differential evolution (DE). The data acquired from two key sources – the Moderate Resolution Imaging Spectroradiometer (MODIS) Deep Blue and the Ozone Monitoring Instrument (OMI) – are incorporated into the hybridized model, along with relevant data from field surveys and dust samples. Goodness-of-fit analyses are performed to evaluate the predictive capability of the hybridized models using different statistical criteria, including the true skill statistic (TSS) and the area under the receiver operating characteristic curve (AUC). The results demonstrate that the hybridized ANFIS-DE model (with AUC = 84.1%, TSS = 0.73) outperforms the other comparative hybridized models tailored for dust-storm prediction. The results provide evidence that the hybridized ANFIS-DE model should be explored as a promising, cost-effective method for efficiently identifying the dust-source areas, with benefits for both public health and natural environments where excessive dust presents significant challenges.

The proliferation of 3D printing MakerSpaces in university settings has led to an increased risk of student and technician exposure to ultrafine particles. New MakerSpaces do not have standardized specifications to aid in the design of the space; therefore, a need exists to characterize the impacts of different engineering controls on MakerSpace air quality. This study compares three university MakerSpaces: a library MakerSpace operating ≤4 devices under typical office space ventilation with no engineering controls, a laboratory MakerSpace operating 29 printers inside grated cabinets, with laboratory-grade ventilation, and a center MakerSpace operating ≤4 devices with neither engineering controls nor internal ventilation. All MakerSpaces were studied under both controlled (using a standard print design) and uncontrolled (real-time user operation) conditions measuring emitted particle concentrations in the near-field. Additionally, volatile organic emissions and the difference between near-field and far-field particle concentrations were investigated in multiple MakerSpaces. The center MakerSpace had the greatest net increase in mean particle number concentration (+1378.9% relative to background during a print campaign using polylactic acid (PLA) filament in a MakerBot (MakerBot-PLA)). The number-weighted mean diameter had the greatest change relative to background during the library campaign, +37.1% for the Lulzbot-PLA and −56.1% for the Ultimaker-PLA studies. For the standard NIST design with MakerBot-PLA, the laboratory's particle removal ratio was 30 times greater than in the library with open cabinets and 54 times greater when the cabinet doors were closed. The average particle removal rate from the center MakerSpace was up to 2.5 times less efficient than that of the library for the same MakerBot-PLA combination. These results suggest ventilation as a key priority in the design of a new university MakerSpace.

High atmospheric aerosol densities in China have significant influence on air quality and human health. It is critical to understand their variations with seasons, air qualities and regional meteorological conditions, not only in the horizontal dimension but also in the vertical dimension. In this study, we discussed aerosol vertical profiles from spaceborne lidar in different seasons, regional air mass movement patterns and ground-level PM2.5 concentrations in three large China cities of Beijing, Shanghai and Guangzhou. The extinction coefficient profiles exhibited apparent seasonal variations due to varied emission and meteorological conditions. Characterized by backward trajectories, the air mass movements, especially the moving speeds and their source regions, have strong impacts on both extinction coefficient values and its relative profile structures. The aerosol extinction coefficient values had positive correlation with ground-level PM2.5 concentrations within the altitude below 700 m, while the relationship in higher altitudes was inapparent. As the altitude increased, the mean extinction coefficient values of high PM2.5 clusters sharply decreased, while the extinction coefficient values of low PM2.5 clusters declined in a linear pattern with a gentle slope. This study provided insightful analysis to understand the vertical distributions of aerosol extinction coefficients under different scenarios, which is helpful for better understanding air pollution episodes.

Accurate and rapid predictions of air pollutant dispersion are important for effective emergency responses after sudden air pollution accidents (SAPA). Notably, dispersion parameters (σ) are the key variables that influence the simulation accuracy of dispersion models. Empirical dispersion schemes based on power-law formulas are probably appropriate choices for simulations in SAPA because of the requirement for only routine meteorological data. However, performance comparisons of different schemes are lacking. In this study, the performances during simulations of air pollutant dispersion of four typical empirical parameterised schemes, i.e. BRIGGS, SMITH, Pasquill-Gifford, and Chinese National Standard (CNS), were investigated based on the GAUSSIAN plume model with datasets for the classic Prairie Grass experiments, 1956. The performances when simulating peak and overall concentrations in different Pasquill atmospheric stability classes (A, B, C, D, E, F) were quantitatively analysed through different statistical approaches. Results showed that the performances of four schemes for peak and overall concentrations were basically consistent. Scheme CNS in unstable atmospheric conditions (A, B, and C) performed significantly better than the others according to performance criteria, which included the lowest mean of absolute value of fractional biases, lowest normalised mean square errors, and largest mean values of the fraction within a factor of two when predicting peak and overall concentrations, respectively. Schemes BRIGGS and P-G exhibited slightly better performances during the neutral condition (D) followed by scheme CNS. Schemes SMITH and CNS demonstrated slight merits in predicting concentrations compared to the other schemes during stable conditions (E and F). As a whole, scheme CNS generally performed well for the different atmospheric stability classes. These analysis results can help to fill in the data gaps and improve our understanding of the influence of typical power-law function schemes on simulations of air pollutant dispersion. The results are expected to provide scientific support for air pollution predictions, especially during emergency responses to SAPA.

To accelerate the deployment of Carbon Capture and Storage (CCS) based on the solid amine adsorbents towards a practical scale application relevant to Natural Gas Combined Cycle (NGCC) power plants, this study has evaluated the cyclic performance of a polyethylenimine/silica adsorbent of kg scale in a laboratory scale bubbling fluidized bed reactor. A high volumetric concentration 80−90 vol% of steam mixed with N2 and CO2 has been used as the stripping gas during a typical temperature swing adsorption (TSA) cycle. Both the simulated NGCC flue gas and the actual flue gas from a domestic gas boiler have been used as the feed gas of the CO2 capture tests with the solid adsorbent. Various characterization has been carried out to elucidate the possible reasons for the initial capacity decline under the steam regeneration conditions. The effect of presence of CO2 in the stripping gas has also been studied by comparing the working capacities using different regeneration strategies. It has been demonstrated that the breakthrough and equilibrium CO2 adsorption capacities can be stabilized at approximately 5.9 wt% and 8.6 wt%, respectively, using steam regeneration for both the simulated and actual natural gas boiler flue gases. However, using a concentration of 15 vol% CO2 in the stripping gas has resulted in a significantly low working capacity at a level of 1.5 wt%, most likely due to the incomplete CO2 desorption and degradation in a CO2 containing environment.

Free-tropospheric aerosol layers and their seasonal variation over Wuhan (30.5°N, 114.4°E), China, are presented based on a 532-nm polarization lidar measurements on 162 days from January through December 2013. Using the aerosol layer selection criterions, a total of 402 free-tropospheric aerosol layer events were identified. The bottom height of the aerosol layers below 2 km accounts for 68% of the total, while approximately 76% of the layer's top height ranges from 1 km to 4 km. Out of the 402 events, 269 (67%) are optically-thin layers with aerosol optical depth (AOD) less than 0.1. The free tropospheric AOD2-7 contribute ∼13–31% to the AOD0-7 and the free-tropospheric aerosol layers show considerable moderate variation. The aerosol layers have the maximum mean geometrical thickness of 1.2 km in spring, while the minimum mean thickness is 0.7 km in autumn, and the mean thickness is 0.93 km and 1 km in summer and winter, respectively. The mean backscatter coefficient of aerosol layers during spring, summer, autumn and winter were 1.8 ± 1.4 Mm−1sr−1, 2.3 ± 2 Mm−1sr−1, 2.8 ± 2.7 Mm−1sr−1 and 2.3 ± 2.2 Mm−1sr−1, respectively. Aerosol layers in different seasonal are classified by particle depolarization ratio, there are a large amount of non-spherical particles and mixed particles present in spring, autumn and winter, and the mean particle polarization ratio of aerosol layers during spring, summer, autumn and winter were 0.22, 0.06, 0.15 and 0.14, respectively.

The OMI NO2 standard product, OMNO2d, has been widely used in estimating surface NO2 concentrations. The Peking University Ozone Monitoring Instrument NO2 product (POMINO) is claimed to provide an improved quality over east-central China. This study estimated one year (Dec.2016–Nov.2017) of surface NO2 concentrations at satellite overpass time based on OMNO2d data and POMINO data, respectively. We used an extra-trees (ET) regression model to convey the non-linear relationship between surface NO2 and predictors, and compared the prediction accuracy with that of random forests (RF) regression model. The ET model showed a better estimation performance than the RF model, with the cross-validation R2 of 0.72 (RMSE = 9.20 μg/m3) and R2 of 0.70 (RMSE = 9.42 μg/m3) based on POMINO and OMNO2d data, respectively. The POMINO-derived monthly mean surface NO2 concentrations were closer to ground NO2 measurements than that OMNO2d-derived. Although the estimations from both satellite products were underestimated in polluted situations, the use of POMINO reduced the underestimation as compared to the use of OMNO2d data.

In this study, we investigated the impact of biomass burning on the oxidative potential of PM2.5 in the metropolitan area of Milan, Italy. PM2.5 samples were collected on quartz filters during cold (December 2018–February 2019) and warm (May 2019–July 2019) seasons at the Municipality of Bareggio, a small town located approximately 14 km northwest of the Milan city center. The PM2.5 constituents were chemically analyzed, and its corresponding oxidative potential was measured by means of the dithiothreitol (DTT) assay. Total PM2.5 mass concentration was significantly higher in winter (71.82 ± 4.17 μg/m3) compared to summer (16.67 ± 0.27 μg/m3), mainly a result of enhanced biomass burning emissions combined with higher atmospheric stability and lower mixing during the cold season. The enhanced biomass burning activities during the winter period also resulted in very high polycyclic aromatic hydrocarbons (PAHs) concentrations (72.81 ± 16.59 ng/m3) which were more than 150-fold higher than the warm period values (0.40 ± 0.07 ng/m3). PAH concentrations were highly correlated with chemical markers of biomass burning (i.e., levoglucosan (R2 = 0.79), and K+/K (R2 = 0.87)) in the winter period. Spearman correlation analysis between DTT and PM2.5 chemical species showed a dominant role of secondary organic aerosols (SOA) and vehicular emissions in summer-time PM2.5 oxidative potential (i.e., the capacity of PM2.5 species to oxidize target molecules), while in the wintertime, the DTT values were highly correlated with chemical markers of biomass burning, vehicular activities, and re-suspended road dust. Multiple linear regression (MLR) analysis identified biomass burning (41%) as the dominant contributor to DTT, followed by SOA (20%), re-suspended road dust (18%), and vehicular emissions (16%). Our results underscore the importance of biomass burning to the overall oxidative potential of PM2.5 in the metropolitan area of Milan, urging the need to promulgate effective mitigation policies targeting these emissions.

The dust has direct effects on people's health and climate change; so, this research studied the remotely sensed dust deposition in Africa from 1980 to 2018, and the dust's particulate matter of 2.5 μm size (or PM2.5), in particular, which pollutes the breathable air. PM2.5 is studied in comparison with multispectral carbon monoxide (CO), an abundant atmospheric air pollutant in central Africa. CO is an atmospheric gaseous pollutant for which the smoke, a gaseous aerosol from incomplete combustion processes, is the biggest source. The literature clarifies that both the particulate matter and the CO endanger human health while breathed in. The dust from the desert of Sahara is windblown all over the world. CO, in Africa, is from the anthropogenic fire and volcanic eruptions' smoke; these are two good reasons to have focused on Africa. Due to the big size of Africa, five sub-regions are set; these are the western, central, northern, eastern and southern sub-regions. The Goddard interactive online visualization and analysis infrastructure (GIOVANNI) has been a bridge to the collected remote sensing data, in this research. The data was collected online, from the measurement of pollution in the troposphere (MOPITT) as well as a second version of the modern era retrospective analysis for research and applications (MERRA-2); the analysis was done by a joint of the software tools, worth noting is the Arc GIS. As the amount of African dust dramatically increased by 2000; the heaviest in 2004, results are based on the selected dust deposition over 2000–2018: time-averaged maps, correlations, and quantitative estimations are reported in this research. The heaviest annual dust deposition reached 25.3 t/km2 over the year 2004, in Liberia, a focal point of study for the western sub-region. An important finding: the dust's PM2.5 positively correlated with multispectral CO from November to May; the positively high correlation coefficient was 0.86 in April 2018. The negative correlation between the two measurements started from June to October; the negatively high correlation was −0.68 in October 2015; this research discussed the possible reasons. This research recommends some onsite studies about the real figures and facts about the dust's effects on health, in all the seasons; thus, an alert to policymakers who would set some strategies to mitigate the dust hazards on the health of African inhabitants, neighbors, and visitors.

Odours associated with hydrogen sulphide (H2S) have been a concern in the municipality of Sibaté (Colombia) over the last decades. With the aim of determining the odour impact of Muña's reservoir, different H2S emission sources were evaluated and simulated. H2S surface emission rates from Muña's reservoir were estimated using the Flux Chamber experimental method. Meteorological conditions and emission rates were then fed to the AERMOD® model to identify the contribution of Muña's reservoir on H2S concentration in Sibaté’s urban area. These results were compared and calibrated with real time H2S atmospheric concentration data. The average H2S emission rate was 1,886 μg/(min·m2), which primarily affects the municipality when wind direction is towards the south. The results of this study demonstrated the applicability of the AERMOD® model and flux chamber measurements in predicting H2S behaviour in the Sibaté region.

Exposure to vehicular emissions has been linked to numerous adverse health effects. In response to the arising concerns, near-road monitoring is conducted to better characterize the impact of mobile source emissions on air quality and exposure in the near-road environment. An intensive measurement campaign measured traffic-related air pollutants (TRAPs) and related data (e.g., meteorology, traffic, regional air pollutant levels) in Atlanta, along one of the busiest highway corridors in the US. Given the complexity of the near-road environment, the study aimed to compare two near-road monitors, located in close proximity to each other, to assess how observed similarities and differences between measurements at these two sites inform the siting of other near-road monitoring stations. TRAP measurements, including carbon monoxide (CO) and nitrogen dioxide (NO2), are analyzed at two roadside monitors in Atlanta, GA located within 325 m of each other. Both meteorological and traffic conditions were monitored to assess the temporal impact of these factors on traffic-related pollutant concentrations. The meteorological factors drove the diurnal variability of primary pollutant concentration more than traffic count. In spite of their proximity, while the CO and NO2 concentrations were correlated with similar diurnal variations, pollutant concentrations at the two closely sited monitors differed, likely due to the differences in the siting characteristics reducing the dispersion of the primary emissions out of the near-road environment. Overall, the near-road TRAP concentrations at all sites were not as elevated as seen in prior studies, supporting that decreased vehicle emissions have led to significant reductions in TRAP levels, even along major interstates. Further, the differences in the observed levels show that use of single near-road observations will not capture pollutant levels representative of the local near-road environment and that additional approaches (e.g., air quality models) are needed to characterize exposures.

Process analyzers for in-situ monitoring give advantages over the traditional analytical methods such as their fast response, multi-chemical information from a single measurement unit, minimal errors in sample handing and ability to use for process control. This study discusses the suitability of Raman spectroscopy as a process analytical tool for in-situ monitoring of CO2 capture using aqueous monoethanolamine (MEA) solution by presenting its performance during a 3-day test campaign at PACT pilot plant in Sheffield, UK. Two Raman immersion probes were installed on lean and rich streams for real time measurements. A multivariate regression model was used to determine the CO2 loading. The plant performance is described in detail by comparing the CO2 loading in each solvent stream at different process conditions. The study shows that the predicted CO2 loading recorded an acceptable agreement with the offline measurements. The findings from this study suggest that Raman Spectroscopy has the capability to follow changes in process variables and can be employed for real time monitoring and control of the CO2 capture process. In addition, these predictions can be used to optimize process parameters; to generate data to use as inputs for thermodynamic models, plant design and scale-up scenarios.

The shear strength and hydraulic permeability of the interface between well cement and casing was investigated using a triaxial direct shear apparatus. For the first time, these experiments provide measurements under controlled stress conditions with fluid flow measurements along the interface. The low cohesion (1.1 ± 1.1 MPa) and the high friction angle (43.4 ± 2.0°) indicates that the shear strength of the interface is provided by friction. This implies that the state of stress of the cement is critical to well integrity. The hydraulic aperture of the undamaged cement-steel samples was 6.8 ± 1.0 microns. Shear damage to the interface caused a decrease (-20 %) in hydraulic aperture for samples aged up to 1 month, and an increase (+300 %) for samples cured for two years. We performed numerical simulations to estimate the leakage potential from a carbon storage operation. This model predicts negligible leakage amounts (47 tonnes) in a shear-damaged well for the modeled injection of ∼1.26 million tonnes of CO2. Thus, our measurements indicate that the cement-casing interface is not a significant leakage pathway in its intact or damaged state, and that shear-driven failure scenarios for this interface are not a significant risk to CO2 storage security.

Previous research has investigated the spatial disparity of ambient PM2.5 concentrations in the context of environmental justice (EJ). However, source emissions associated with the PM2.5 disparity have not been well understood. In this study, we found 2.54 μg/m3 (40.9%, p < 0.0001) higher PM2.5 concentration, on average, in more vulnerable (MV) communities than in less vulnerable (LV) communities in California for the period 2012–2014. Multiple linear regression models were employed to quantify the contributions of on- and off-road vehicles and point source emissions to the PM2.5 disparity between MV and LV communities while adjusting for local meteorology and site-specific characteristics. Controlling for on-road vehicular emissions associated with PM2.5 reduced the spatial PM2.5 disparity the most between MV and LV communities down to 1.05 μg/m3 (p = 0.0105), followed by off-road vehicular emissions (1.75 μg/m3, p < 0.0001) and PM2.5 point source emissions (2.17 μg/m3, p < 0.0001). The comparison of the full (including 3 emission predictors together) and reduced (including 2 emission predictors) models also demonstrated the strongest association of on-road vehicular emissions with the observed PM2.5 disparity. The largest contribution of on-road vehicular emissions to PM2.5 disparity seems to be attributable to disproportionately higher road density, especially for limited access roads such as Interstate highways (a factor of 1.76–2.39) and higher traffic volume (a factor of 1.62) in MV communities. These findings suggest that continuing efforts to reduce on-road traffic emissions and consider the spatial relation between MV communities and high-traffic roadways may be beneficial to alleviate potential PM2.5 health risks, particularly in MV communities.

The mercury (Hg) research community is in need of a method to quantify reactive, gaseous oxidized, and particulate-bound Hg compounds. The University of Nevada, Reno-Reactive Mercury Active System (UNR-RMAS) was designed to quantify reactive Hg, as well as identify compounds present in the atmosphere. This system has undergone significant improvements and is now designated as UNR-RMAS 2.0. The system physical design, flow management, and sample analytical methods have been improved. A new sample manifold increased reliability and consistency of air flow. The thermal desorption method for identification of gaseous oxidized Hg compounds was improved with respect to temporal resolution and temperature management. A statistical method was developed that allows for quantifying reactive Hg (RM) compounds. In addition, analyses of anions on nylon membranes was investigated as means of understanding air mass chemistry and potential RM compounds. The results of these improvements are demonstrated through comparison of a year of UNR-RMAS 2.0 sample data collected in 2018–2019 with that collected in 2014–2015. Implemented changes resulted in improved sample replication and resolution of RM quantification and speciation.

Decision of emergency response to releases of hazardous material in the atmosphere increasingly rely on numerical simulations. This paper presents two contributions for accounting for the uncertainty inherent to those simulations. We first focused on one way of modelling these uncertainties, namely by applying stochastic perturbations to the inputs of the numerical dispersion model. We devised a generic mathematical formulation for time dependent perturbation of both amplitude and dynamics of the inputs. It allows a more thorough exploration of possible outcomes than simpler perturbations found in the literature. We then improved on the current state of the art on dimension reduction of atmospheric data. Indeed, most statistical methods cannot cope with high dimensional data such as the maps simulated with atmospheric dispersion models. Principal component analysis, the most widely used method for dimension reduction, relies on a linearity hypothesis that is not verified by these sets of maps. We conducted a very encouraging experiment with auto-associative models, a non-linear extension of this method.

Subsurface reservoirs are targeted formations for geologic carbon dioxide (CO2) storage. Even if proper management of injection pressures minimizes the risks of induced seismicity, high pressure CO2 can interact with brine-saturated host rock and cause microstructural changes that lead to alterations in poromechanical properties of the rock. The effect is well pronounced in carbonate-rich rock, but observations on silica-rich reservoirs are ambiguous. In this study, we report a broad range of experiments performed on Berea sandstone, consisting mainly of quartz (∼90%), in three different states: pristine, thermally damaged, and thermally damaged then treated with liquid CO2. Drained and undrained poromechanical tests, ultrasonic velocity measurements, acoustic emission (AE), X-ray diffraction (XRD), and petrographic analyses are conducted. The tests reveal that thermal damage alone does not significantly affect poromechanical properties. However, CO2 injection does affect strength (10–15 % decrease), permeability (up to 100% increase), porosity (10% increase), and elastic creep rate (more than twice); corresponding microstructural changes were observed from XRD test results. At the same time, the poroelastic moduli measured in triaxial compression experiments and load-induced fracture processes, as interpreted through acoustic emission data collected in uniaxial compression tests, were affected insignificantly. These experimental observations provide better understanding of the mechanical behavior of low-carbonate reservoir rocks that are subjected to high pressure CO2 injection.

In China, a significant reduction in primary pollution has been observed due to the Clean Air Action since 2013, and ozone pollution has become increasingly prominent over the past years. Pearl River Delta (PRD) is one of the most successful regions concerning primary pollution control, while is suffering from severe ozone pollution during autumn. In this study, we present a field campaign in Shenzhen, a megacity in PRD, in October 2018 with measurements of ozone and photochemical precursors. These observational data are helpful to analyze the local ozone budget and its sensitivity to precursors with the help of an observation-based model (RACM2-LIM1). The observed ozone concentration was up to 121 ppbv during a photochemical episode from 1 to 8 October, when intensive ozone formation up to tens of ppbv/h was found. Ozone vertical measurement indicates the fast ozone production is happening throughout the planetary boundary layer (PBL), which is an important source of morning ozone increase resulting in ozone pollution. An explicit case study is performed to reveal the diurnal feature of instantaneous ozone production rate (P(Ox)) and accumulative P(Ox) based on the O3-NOx-VOC sensitivity, ROx radical primary production rate (P (ROx)), and LN/Q for three cases including ozone pollution and attainment periods. Results show that nitrogen oxides (NOx = NO + NO2) reduction have positive and negative impact on local ozone production from one pollution episode to the other, which indicates the complexity of O3-precursors sensitivity and difficulty to control ozone pollution in Shenzhen. Finally, comparison among measurements in other campaigns provides additional evidence on local ozone production sensitivity on NOx and anthropogenic volatile organic compounds (AVOCs) with respect to a temporal and spatial change. The NOx reduction in Shenzhen has led to higher ozone production from 2015 to 2018 given the nearly constant VOC. However, the ozone mitigation would be benefit from further NOx reduction in the conditions of 2018.

Dust storms are considered as one of the most important environmental challenges in the West Asia region. In addition to the harmful impacts of dust storms on human health, they also have particular effects on socioeconomic and agroecological domains of human communities. Identify the sources of dust storms is the first step to combat against these devastating phenomena. Accordingly, the present study was conducted to determine dust sources of the Tigris and Euphrates basin using satellite and climatic data. Monthly LST and NDVI of MODIS, monthly wind speed, soil moisture, and absolute air humidity data from GLDAS, monthly TRMM precipitation, and soil texture data of FAO were used. The Analytic Hierarchy Process (AHP) model was applied to determine the weights of the collected data (i.e. criteria or drivers for dust storms formation). Susceptible Areas to Dust Storm Formation (SADSF) were determined using the Weighted Linear Combination (WLC) model for months of June, July, and August from 2000 to 2017. After performing SADSF analysis, five main dust sources were identified in the whole basin. To evaluate the accuracy of the results, the number of real Observed Dust Storms (ODS) in each source was compared to the repetition of allocation in SADSF for each pixel over the 18-year period of this study from 2000 to 2017. Results indicated that the area of SADSF has significantly grown for all three months since 2008. The areas of SADSF in June and July were almost the same, while they were significantly bigger than August. Among identified dust sources, the highest SADSF repetition was in the northwest of Iraq followed by eastern Syria, southern Iraq, southeast border of Iraq, and east border of Iraq, respectively. The correlation coefficient between the SADSF repetition with the number of ODS events in those recognized dust sources was equal to 0.88, 0.76, and 0.74 for June, July, and August, respectively, that shows the accuracy of our results in comparison to actual data.

Carbon Capture and Storage has the potential to make a significant contribution to the mitigation of climate change, however there is a regulatory and societal obligation to demonstrate storage robustness and minimal local environmental impact. This requires an understanding of environmental impact potential and detectability of a range of hypothetical leak scenarios. In the absence of a significant body of real-world release experiments this study collates the results of 86 modelled scenarios of offshore marine releases derived from five different model systems. This synthesis demonstrates a consistent generalised relationship between leak rate, detectability and impact potential of a wide range of hypothetical releases from CO2 storage, which can be described by a power law. For example a leak of the order of 1 T per day should be detectable at, at least, 60 m distance with an environmental impact restricted to less than a 15 m radius of the release point. Small releases are likely to require bottom mounted (lander) monitoring to ensure detection. In summary this work, when coupled with a quantification of leakage risk can deliver a first order environmental impact assessment as an aid to the consenting process. Further this work demonstrates that non-catastrophic release events can be detected at thresholds well below levels which would undermine storage performance or significantly impact the environment, given an appropriate monitoring strategy.

CO2 sequestration into saline aquifers has been demonstrated as an effective technique to mitigate the effects of carbon dioxide on the atmosphere. The displacement mechanism during this process has not been clarified and the two-phase immiscible flow is affected by many factors. In this study, two types of homogeneous micromodels with circular and square cross sections were used to investigate the pore-scale of residual and capillary trapping at 25 ℃ and ambient pressure. Two salinities and six injection rates were used to study their impacts on CO2 saturation. Drainage experiments were conducted using a high-resolution microscope and a camera. The CO2 saturation and its distribution are investigated using image processing. Three forms of wetting phases are observed in circular grains, whereas additional special forms are observed in square grains, and these existing forms of the wetting phase are applied for mechanism analysis. Changes in the tortuosity and wettability are also analyzed to clarify why the saturation in the micromodel with square cross section was higher than that of circular cross section. The displacement pattern, the injection rate, the salinity, and the micromodel structure all have impacts on CO2 displacement efficiency and safe sequestration.

The general acceptance of the CO2 geological storage by stakeholders passes through the assessment and mitigation of risks, potentially induced or increased by the disposal activity. Injection of moderate to large quantities of CO2 in the sub-surface may unbalance local stress and trigger earthquakes if faults are critically stressed, condition that is not easily verifiable. Pilot sites are therefore the best way to proceed further in order to address such challenging issues. In such cases, the reconnaissance of faults and seismicity in the sub-surface, before the onset of activity, is mandatory. In this paper, we present studies carried out in the site where the Sotacarbo Fault Lab is going to be installed. This facility will be located in a very low seismic hazard region of central Mediterranean, where reports on historical large earthquakes are poor. We show results from a series of experiments aimed to monitor the background seismicity around the pilot site. As expected, seismicity is almost absent down to small magnitude close to the future injection-test well. Further seismic imaging of the sub-surface layers obtained by ambient noise tomography offers the ability to resolve the presence of a seismicity-free fault located in the first 200 m below the surface, of which the last episode of activity is difficult to assess. Our results encourage the use of this site to follow the response of the system to injection of small quantity of CO2.

The weekly cycle and weekend effect in the O3, NO, NO2, CO, CH4, SO2, NMHC, and PM10 concentrations were investigated in the Moscow megacity using in-situ measurements from January 1, 2005 to December 31, 2014 at 49 stations of the Moscow Environment Monitoring network. Daily variations in the CO, NOx, NMHC, and PM10 concentrations depend mainly on motor transport emissions and the atmospheric boundary layer vertical stratification. The characteristic feature of Moscow is the time coincidence of rush hours and surface temperature inversion during the cold season, which results in pollutant accumulation in the atmospheric surface layer. It was found that the surface concentrations of the pollutants (except ozone and methane) decrease on weekends. Weekday (Tuesday–Friday)-Sunday differences in the daytime (08:00–22:00 LT) NO, NO2, CO, SO2, NMHC, and PM10 concentrations relative to those of weekday period averaged for all stations over 2005–2014 amounted to 23.9 ± 5.8, 16.7 ± 2.8, 13.6 ± 3.3, 7.6 ± 6.5, 6.3 ± 2.2, and 14.5 ± 5.1%, respectively. The ozone concentration increased on Sunday by 16.5 ± 4.8%. The methane concentration on weekends was the same as on weekdays. The weekend effects in all pollutant concentrations were weakened within the greenbelt around Moscow. In different sectors of Moscow the pollutant weekend effects except that in SO2 were approximately the same. The vertical structure of the NO, NO2, and CO weekend effects was analyzed based on data obtained from measurements at the TV tower 500 m in height. These weekend effects decreased nonlinearly with height. Estimates obtained for basic criteria of activity of photochemical processes determining the formation of the weekly cycle and weekend effect of ozone (NMHC/NOx ratio, fraction of radical loss via NOx chemistry, concentration of Ox) show that the VOC-limited chemistry is characteristic of Moscow.

Atmospheric reactive nitrogen (Nr) emission and deposition have caused much damage to the global environment and also impacted human health. Determining long-term trends in atmospheric Nr concentrations and deposition is important for evaluating air quality and ecosystem effects and formulating control measures to reduce negative impacts. We measured monthly concentrations of NH3, pNH4+, HNO3, pNO3-, and NO2 and calculated their dry deposition rates using an inferential method from 2011 to 2018 at an urban site in Zhengzhou in the North China Plain. Inorganic N (NH4+ and NO3−) concentrations in precipitation and wet/bulk N deposition were also quantified in 2011, 2016, 2017 and 2018. The results showed high concentrations of atmospheric Nr species, with considerable variation in some species over the monitoring period. Annual mean NH3 concentrations significantly increased from 2011 to 2018 (p < 0.001) with that in 2018 being twice that in 2011. However, pNO3- and pNH4+ concentrations decreased after 2013. The annual mean NO2 concentration in 2018 had decreased by 33.4% compared to the peak in 2011. Highest NH3 concentrations were measured in spring or summer, and lowest concentrations in winter. pNH4+ and pNO3- concentrations peaked mainly in winter. Total dry N deposition ranged from 21.9 to 41.2 kg N ha−1. Bulk NH4+ and NO3− deposition was lower in 2016–2018 than in 2011. However, atmospheric Nr concentrations and N deposition remained high in this urban ecosystem. NH3 emissions urgently need control measures, and Nr impacts on the surrounding ecosystems and human health should be considered. Continuous long-term measurements of atmospheric Nr concentrations and deposition at a regional scale are necessary to help formulate emission control measures and protect human and ecosystem health.

The air quality of South Korea was the focus of the NASA/NIER KORean -United States Air Quality (KORUS-AQ) mission of 2016. KORUS-AQ was planned for the period after the spring peak in outbreaks of Asian dust. Regardless of this strategic planning, quantifiable dust was still observed via instrumentation on the NASA DC-8 in early May. A novel analysis of supermicron dust and associated supermicron ionic relationships was completed using two size dependent instruments. This supermicron dust provided surface area for heterogeneous chemistry between CaCO3, the gases HNO3, NO2, and SO2, and particulate (NH4)2SO4. Uptake of the pollutant gases is greatly enhanced by formation of an aqueous layer on the surface of the dust particles. More water is attracted to particles where uptake of HNO3 has replaced surface CaCO3 with Ca(NO3)2 generating a dynamic aqueous layer on the dust particle. We propose that particulate (NH4)2SO4 coagulated with dust to form (NH4)2Ca(SO4)2 on the particle surface, which rapidly formed CaSO4 and NH4+ in a Ca(NO3)2 facilitated aqueous layer. A conceptual model is proposed to explain these dust uptake chemical processes. We define the nanoequivalent concentration of supermicron SO42− plus NO3− over the nanoequivalent concentration of supermicron NH4+ plus Ca2+ as the Dust Pollution Index (DPI), used to quantify the extent to which carbonate dust has been modified. DPI values range from 0 (pure dust) to 1 (completely reacted); thus, it represents the conversion of CaCO3 into secondary salts. This mechanism should be used to better predict chemical dynamics in atmospheric models while also helping to further explain the importance of dust and secondary coating on cloud formation processes and dust optical properties. Air masses containing dust that traversed industrial China while mixing with polluted southern air had significantly higher DPI values (average = 0.82, 1σ = 0.10) compared to air masses that limited interaction with such pollution (average = 0.51, 1σ = 0.13).

In this study, the performance of both surface and borehole time-lapse gravity monitoring to detect CO2 leakage from a carbon storage site is evaluated. Several hypothetical scenarios of CO2 migration in a leaky fault, and thief zones at different depths at the Kimberlina site (California, USA) constitute the basis of the approach. The CO2 displacement is simulated using the TOUGH2 simulator applied to a detailed geological model of the site. The gravity responses to these CO2 plumes are simulated using forward modeling with sensors at ground surface and in vertical boreholes. Results of inversion on one scenario are also presented. The surface-based gravity responses obtained for the different leakage scenarios demonstrate that leakage can be detected at the surface in all the scenarios but the time to detection is highly variable (10–40 years) and dependent on the detection threshold considered. Borehole measurements of the vertical component of gravity provide excellent constraints in depth when they are located in proximity of the density anomaly associated with the presence of CO2, thus discriminating multiple leaks in different thief zones. Joint inversion of surface and borehole data can bring valuable information of the occurrence of leakages and their importance by providing a reasonable estimate of mass of displaced fluids. This study demonstrates the importance of combining multiphase flow simulations with gravity modeling in order to define if and when gravity monitoring would be applicable at a given storage site.

Interannual variation of the aerosol optical depth (AOD) in East Asia has been investigated using Moderate Resolution Imaging Spectroradiometer (MODIS) data and Modern Era Retrospective analysis for Research and Applications Version 2 (MERRA-2) data for 2000–2018. The data analysis focuses on boreal spring when Siberian biomass burning is at its seasonal maximum. The results indicate that the significant increase in organic and black carbon is primarily caused by emissions from biomass burning in East Asia, which leads to significant interannual variations in aerosol loading and pan-Pacific transport. The anomalous large-scale climate variability associated with the East Asia Jet Stream (EAJS) provides favorable conditions for increasing the AOD of organic and black carbon in Northeast Asia and may represent an underlying physical mechanism. When the EAJS shows greater weakening than normal, abnormal high-pressure anomalies are maintained in East Asia, which tend to drive warm advection over Northeast Asia. This warm advection expedites the melting of the Eurasian snow cover, which helps increase surface dryness in late spring and provides favorable conditions for biomass burning. The EAJS index can be predictable with statistical significance up to lead 1 month by the dynamical ensemble seasonal forecasts, suggesting a possible implementation of the empirical AOD forecasts using climate forecast models.

Black carbon (BC) is an important contributor to global particulate matter emissions. BC is associated with adverse health effects, and an important short-lived climate pollutant. Here, we describe a low cost method of analysis that utilizes images of PTFE filters taken with a digital camera to estimate BC content on filters. This method is compared with two existing optical methods for analyzing BC (Smokestain Reflectance and Hybrid Integrating Plate and Sphere System) as well as the standard chemical analysis method for determining elemental carbon (Thermal-Optical Reflectance). In comparisons of aerosol generated under controlled conditions (using an inverted diffusion flame burner to cover a range of mass loading and reflectance levels) (N = 12) and in field samples collected from residential solid fuel combustion in China and India (N = 50), the image-based method was found to correlate well (normalized RMSE <10% for all comparisons) with existing methods. A correlational analysis of field samples between the optical methods and Fourier-transform infrared spectroscopy indicated that the same functional groups were predominantly responsible for light attenuation in each optical method. This method offers reduced equipment cost, rapid analysis time, and is available at no cost, which may facilitate more measurement of BC where PM2.5 mass concentrations are already measured, especially in low income countries or other sampling efforts with limited resources.

Rural lower Yakima Valley, Washington is home to the reservation of the Confederated Tribes and Bands of the Yakama Nation, and is a major agricultural region. Episodic poor air quality impacts this area, reflecting sources of particulate matter with a diameter of less than 2.5 μm (PM2.5) that include residential wood smoke, agricultural biomass burning and other emissions, truck traffic, backyard burning, and wildfire smoke. University of Washington partnered with the Yakama Nation Environmental Management Program to investigate characteristics of PM2.5 using 9 months of data from a combination of low-cost optical particle counters and a 5-wavelength aethalometer (MA200 Aethlabs) over 4 seasons and an episode of summer wildfire smoke. The greatest percentage of hours sampled with PM2.5 >12 μg/m3 occurred during the wildfire smoke episode (59%), followed by fall (23%) and then winter (21%). Mean (SD) values of Delta-C (μg/m3), which has been posited as an indicator of wood smoke, and determined as the mass absorbance difference at 375–880 nm, were: summer – wildfire smoke 0.34 (0.52), winter 0.27 (0.32), fall 0.10 (0.22), spring 0.05 (0.11), and summer – no wildfire smoke 0.04 (0.14). Mean (95% confidence interval) values of the absorption Ångström exponent, an indicator of the wavelength dependence of the aerosol, were: winter 1.5 (1.2–1.8), summer – wildfire smoke 1.4 (1.0–1.8), fall 1.3 (1.1–1.4), spring 1.2 (1.1–1.4), and summer – no wildfire smoke 1.2 (1.0–1.3). The trends in Delta-C and absorption Ångström exponents are consistent with expectations that a higher value reflects more biomass burning. These results suggest that biomass burning is an important contributor to PM2.5 in the wintertime, and emissions associated with diesel and soot are important contributors in the fall; however, the variety of emissions sources and combustion conditions present in this region may limit the utility of traditional interpretations of aethalometer data. Further understanding of how to interpret aethalometer data in regions with complex emissions would contribute to much-needed research in communities impacted by air pollution from agricultural as well as residential sources of combustion.

Understanding the heterogeneity between ambient concentration and personal exposure is crucial in studies regarding the health risks of air pollution exposure. We performed a panel study with 4–19 (average = 10) repeated personal monitoring in 16 adult subjects (ages 18–30) for three consecutive weeks during the winter and summer of 2011–2012 in the Chinese megacity of Guangzhou. Also, we conducted simultaneous ambient measurements at eight districts (including five urban sites, two suburban locations, and one rural site) of Guangzhou. Significant seasonal variations were shown in personal PM2.5 exposure for most of the analyzed components (p < 0.05), with higher levels in winter than in summer. Average personal exposures exhibited a pattern of central urban > suburban > rural areas for PM2.5 mass and most of the constituents (e.g., carbonaceous aerosols, ions). We applied mixed-effects models to estimate within- and between-subject variance components and determinants of personal PM2.5 exposure after adjusting for potential confounders. The within-subject variance component dominated the total variability (63.7–95.6%) for most of the investigated PM2.5 components. Ambient PM2.5 mass and its components were the dominant predictors and contributors of the corresponding personal exposures (0.11 < Rc2 < 0.97; p < 0.05). The results indicate that season and district type affect personal PM2.5 exposure and its components, contributing to 4.9–51.6% and 8.0–77.8% of the variability. Time indoors and outdoors were also factors affecting personal exposure. The study findings revealed ambient concentrations at a fixed monitoring station underestimated residents’ true exposure levels. In conclusion, the current study emphasizes the need for incorporating spatio-temporal activity patterns complementing evenly-distributed air quality monitoring networks to increase the estimation power in epidemiological analysis linking true personal exposure to health effects.

Airborne particulate matter (PM) mass concentrations measured by conventional monitoring stations are reliable data sources for air quality communication, pollution mapping, and exposure estimation. A high spatiotemporal density of monitoring stations can provide a better understanding of PM transport on a regional and global scale and help to reduce exposure misclassification leading to a better assessment of the health impacts associated with PM exposure. However, due to the cost and operational complexities, only a limited number of such PM monitors can be deployed. Apart from conventional measurements, PM mass concentration can be estimated from aerosol optical depth (AOD) data observations by using empirical, semi-empirical or modeling methods, but these datasets are usually compromised by weather conditions and lack of knowledge of aerosol properties. In addition to the above methods, a network of low-cost PM sensors is also a promising approach to increase the measurement density. In this study, we propose an integration of information from multiple measurement approaches. We demonstrate this approach by synergizing the data from 75 monitoring stations, 2,363 Air Box low-cost sensors (the amount of data entries is ∼10 million), and the Terra remote sensing satellite to estimate surface concentrations of PM for Taiwan Main Island during July 14 2018 to Oct 31 in 2018. A machine learning method selects the useful data from the low-cost sensor datasets, and the ordinary Kriging method is used to create a visual daily PM distribution map. The integration of datasets can enhance the overall data quantity and quality, leading to more accurate pollution maps with greater detail. The maps created from these three data sources demonstrate an approximate 30-fold synergistic improvement in the spatial resolution of PM mapping. The Root Mean Square Error (RMSE) of the predicted maps was analyzed through leave-one-out cross-validation, ten-fold cross-validation, and standard data validation. It shows that including low-cost PM sensor data brings in greater detail and largely enhances the spatial distribution while maintaining the pollution mapping characteristics. The approach described here will greatly assist the validation of PM transport models and enhance the accuracy of exposure estimations in future studies.

One potential risk in CO2 sequestration is the leakage of carbon dioxide, which can result in contamination of underground water, creating potential threats to existing ecosystems. The common leakage pathway is through the pre-existing fractures or discontinuities within cement in the wellbore, incurred by the environmental conditions imposed on the cement. Injecting nanoparticles into pre-existing cracks is one of the most recently proposed ideas for mitigating fracture propagation in cement CO2 sequestration. To demonstrate the feasibility of this new technology, a numerical approach was taken in this work, as it is challenging to investigate it in a laboratory setting. We proposed a coupled ALE (Arbitrary Lagrangian–Eulerian)–DEM (Discrete Element Method)–peridynamic modeling strategy within the LS-Dyna package to investigate the intertwined interaction among the CO2 fluid flow, native fluid (e.g., brine), particle clusters, and cracks within the cement. The numerical results demonstrate that injected nanoparticles can effectively reduce the pressure exerted on the crack surface. Accordingly, the potential fracture propagation at the crack tip would be reduced compared to corresponding cases without nanoparticels as pressurized by fluid flow. This result verifies the effectiveness of proposed nanoparticle injection technology. Finally, using this established modeling strategy, the effect of filling particles on the fracture mitigation for different crack geometries (e.g. particle cluster patterns, aspect ratio of crack aperture and length) and CO2 reservoir pressure are examined. The result shows that injected particles successfully reduce the fracture propagation in these scenarios.

Air quality affects both ecosystems and human health. To assess the effects of air pollution, spatially explicit information of pollutants is needed. Atmospheric chemistry transport models are the best option to estimate concentrations and deposition of pollutants from local to regional scales. However, concentration and deposition maps derived from available regional and global models are typically given at spatial resolutions of 10–50 km and do not contain information at sufficiently high spatial resolution (i.e. ≤ 5 km × 5 km) to identify risks and to develop solutions to protect ecosystems and human health. Here we provide deposition and concentrations of nitrogen (N) and sulfur (S) at a 5 km × 5 km resolution for the western Iberian Peninsula. The new maps are a major improvement over existing information due to the higher spatial resolution. Comparisons with measurements indicate that all maps for N compounds are fit for purpose. Nitrogen deposition in W Iberia ranged from 3 to 38.6 kg N·ha−1·year−1, averaging ∼8.2 kg N·ha−1·year−1 with a higher contribution from reduced N forms (62%). Deposition of oxidized forms mainly prevailed in urban and industrial areas and in coastal locations. The contribution of wet deposition was slightly higher (55%) than dry deposition and more important in the North, following the pattern of precipitation. Dry deposition is higher closer to emission sources. Due to their high spatial resolution, these maps can be used for policy development to support ecosystem protection, through the identification of areas at greater risk due to high N deposition. National policy efforts to reduce N pollution must, foremost, target ammonia (NH3) emissions in rural areas and oxidized nitrogen (NOx) emissions in urban and industrialized areas.