Component Of Haze Research Articles

Fine-grained prediction of PM2.5 concentration based on multisource data and deep learning

With China's economic development, air pollution has become increasingly serious. Studying the concentration of PM2.5, as the main component of haze, is the key to control air quality. Extent research lacks spatiotemporal correlations and full regional coverage. Therefore, a fine-grained prediction method was developed for continuous prediction of station-specific and regional PM2.5 concentrations over 24 h that comprise two parts: a site prediction model (TSRT model) and a grid prediction model. The TSRT model includes a long short-term memory-based temporal predictor that uses temporally related features as inputs, a Long Short-Term Memory-based spatial predictor that requires spatially related features as inputs, and a regression model based on regression-tree algorithm to aggregate the predictions of temporal and spatial predictors. The grid concentration prediction model based on a fully connected back-propagation neural network with grid characteristics as inputs achieved a PM2.5 regional prediction with 1-km2 non-overlapping grids across the study area. The approach was evaluated with data from 101 monitoring stations in the Beijing-Tianjin-Hebei area. The results imply that: (1) the forecasting performance of TSRT models is better than other possible methods with average R2 = 0.9 and average MAE = 10.35 μg/m3, (2) R2 and MAE values of the grid prediction model are 0.77 and 21.87 μg/m3, supporting the advantages of our method over well-known interpolation models, such as kriging interpolation, and (3) the method provides appropriate PM2.5 concentration prediction in the next 24 h. Therefore, this novel method may improve PM2.5 prediction, benefiting people's daily lives and influencing policies.

Ammonia-triggered apoptosis via immune function and metabolic process in the thymuses of chickens by proteomics analysis.

Ammonia (NH3), an environmental pollutant with a pungent odor, is not only an important volatile in fertilizer production and ranching, but also main basic component of haze. In present study, we found that ultrastructural changes and 3167 differentially expressed proteins (DEPs) using proteomics analysis in the thymuses of chickens exposed to NH3 on day 42. Obtained DEPs were enriched using GO and KEGG; and 66 DEPs took part in immune function, metabolic process, and apoptosis in the thymuses of chickens treated with NH3. 9 genes of DEPs were validated using qRT-PCR, and mRNA expression of 2 immune-related genes (CTSG and NFATC2), 3 metabolic process-related genes (APOA1, GOT1, and GOLGA3), and 4 apoptosis-related genes (PIK3CD, CTSS, CAMP, and NSD2) were consistent with DEPs in chicken thymuses. Our results indicated that excess NH3 led to immunosuppression, metabolic disorder, and apoptosis in chicken thymuses. Present study gives a novel insight into the mechanism of NH3 toxicity and demonstrated that immune response, metabolism process, and apoptosis were important in the mechanism of NH3 toxicity of chicken exposure to high concentration of NH3.

A method for predicting short‐time changes in fine particulate matter (PM2.5) mass concentration based on the global navigation satellite system zenith tropospheric delay

AbstractIn this study, a method of haze prediction has been developed based on zenith tropospheric delay (ZTD). The relationship between ZTD and fine particulate matter (PM2.5, the main component of haze) during hazy periods in Beijing from 2015 to 2018 was analysed. The correlation between ZTD and the PM2.5 series was analysed based on data from three hazy periods with relatively stable weather conditions, no heavy rainfall and relatively continuous data, and the correlation coefficient between ZTD and the PM2.5 series was maintained in the range [0.5207, 0.7883]. The analysis showed a strong correlation between them based on data from three hazy periods with relatively stable weather conditions. To enhance the correlation between ZTD and the PM2.5 sequence, the standard orthogonal wavelet Daubechies (db5) method was used in this research. By removing the influence of the high frequency signal using the method, the ZTD and PM2.5 sequences were reconstructed using the fourth‐layer low frequency coefficients, their correlation coefficient was maintained in the range [0.5540, 0.9067] and the percentage range of the correlation coefficient was increased to [2.46, 16.69]. Finally, the reconstructed PM2.5, ZTD, relative humidity, average wind speed and NO2 series through db5 were used to establish a multiple regression model to predict the change in the PM2.5 mass concentration. The experimental results show that the multivariate regression model after wavelet analysis is superior to the traditional multivariate regression method in predicting the short‐term change of PM2.5 through four statistics values (R2, F, P, S2) of the regression model and that it is effective and feasible for predicting short‐term haze.

Spatiotemporal variation in PM2.5 concentrations and their relationship with socioeconomic factors in China's major cities

The air quality issues caused by extreme haze episodes in China have become increasingly serious in recent years. In particular, fine particulate matter (PM2.5) has become the major component of haze with many adverse impacts and has therefore become of great concern to scientists, government, and the general public in China. This study investigates the spatiotemporal variation in PM2.5 in 269 Chinese cities from 2015 to 2016 and its associations with socioeconomic factors to identify the possible strategies for PM2.5 pollution mitigation. Specifically, we first quantified the spatial pattern of PM2.5 concentrations in both 2015 and 2016, and then changes between the two years. Next, we examined the relationship between socioeconomic factors and PM2.5 concentrations and changes. The results showed that most cities in eastern China experienced decreases in PM2.5 concentration, although most of these cities already had high PM2.5 pollution level. Cities with low PM2.5 concentrations experienced increases in PM2.5 concentrations and were mostly located in southern and southwestern China. The PM2.5 concentration was the highest in winter, followed by in spring, autumn and summer; for changes in PM2.5 concentrations, the highest magnitude of decrease occurred in summer, followed by the decreases in winter, autumn and spring. Cities with high PM2.5 concentrations tended to be clustered, but the clustered characteristics were not clearly related to the changes in PM2.5 concentrations. The relationship between PM2.5 concentration and urban size was an inverse U-shaped curve, suggesting the existence of the Environmental Kuznets Curve for air quality in China. Population density and secondary industry share are the keys factors relating to air pollution control. In comparison to other cities, most moderately developed cities had a greater magnitude of decrease in PM2.5 concentrations and the key factor for pollution improvement was industrial structure; however, smaller cities tended to have a greater increase in PM2.5 concentrations and population density was the most important influencing factor. As a result, for air pollution control in China, specific regulations should be carried out according to different regions and different developmental stages based on the locations of cities.

Study on the Characteristics of Insulator Leakage Current in Haze Based on FCM Clustering Algorithm

Among the climate problems caused by industrial pollution, haze is particularly serious. Haze not only brings inconvenience to normal life but also brings a lot of problems to the insulation level of the power system. Insulator leakage current reflects all the states in the flashover process of insulators and is an important parameter to evaluate the insulation level of insulators. Different haze conditions were simulated in the laboratory to measure the leakage current of insulators under various haze components. The leakage current of insulators is analyzed by clustering algorithm, and the characteristic values were extracted to obtain the different characteristics of the leakage current of insulators under various haze components. And for the analysis of the results of the clustering algorithm to improve the measures.

Analysis of Insulator Leakage Current under Fog and Haze Conditions Based on K-means Algorithm

When fog and haze weather occurs, insulator flash over accident is easy to happen, which have adverse effects on the safe and stable operation of the power system. The insulators leakage current under different haze components was measured by simulating the haze environment in the laboratory. The K-means algorithm is used to analyze the leakage current of insulators. By comparing the silhouette coefficient of the insulator leakage current under different fog and haze environment, it is found that the silhouette coefficient can be taken as the characteristic value of fog and haze environment. The relationship between the silhouette coefficient of leakage current and the fog and haze environment is summarized.

Application of transcriptome analysis: Oxidative stress, inflammation and microtubule activity disorder caused by ammonia exposure may be the primary factors of intestinal microvilli deficiency in chicken

Ammonia (NH3), an inhaled harmful gas, is not only an important volatile in fertilizer production and ranching, but also the main basic component of haze. However, the effect and mechanism of NH3 on the intestines are still unclear. To investigate the intestinal toxicity of NH3 inhalation, morphological changes, transcriptome profiles and oxidative stress indicators of jejunum in broiler chicken exposed to NH3 for 42 days were examined. Results of morphological observation showed that NH3 exposure caused deficiency of jejunal microvilli and neutrophil infiltration. Transcriptomics sequencing identified 677 differential expressed genes (DEGs) including 358 up-regulated genes and 319 down-regulated genes. Enrichment analysis of obtained DEGs by Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) found that biological functions and pathways affected by NH3 included antioxidant function, inflammation, microtubule and nutrition transport. Relative genes validation and chemical detection confirmed that NH3-induced oxidative stress by activating CYPs and inhibiting antioxidant enzymes promoted inflammatory response and decreased microtubule activity, thus destroying the balance of nutritional transporters. Our study perfects the injurious mechanism of NH3 exposure and provides a new insight and method for environmental risk assessment.

Ammonia induces Treg/Th1 imbalance with triggered NF-κB pathway leading to chicken respiratory inflammation response

Ammonia (NH3) is a severe air pollutant and a component of haze (PM2.5). The respiratory tract is the first route of exogenous ammonia's entry into the human and animal body. Many studies have suggested that exposure to NH3 is associated with a higher risk of respiratory tract tracheitis; however, the underlying mechanism remains unclear. In this study, chicken tracheas were used as a model to investigate toxic effects and genetic changes induced by NH3 exposure, as evaluated by scanning electron microscopy (SEM) and bioinformatic analyses. The transcript analysis illustrated that NH3 exposure caused immune disorders, which play key roles in regulating inflammatory responses from NH3 exposure. Therefore, we carried out Real-time quantitative PCR (RT-PCR) and Western Blot analyses to detect the immune response genes; Treg/Th1, Th2 and Th17 secretions were found that led to inflammatory responses. Next, we also detected the NF-κB pathway and downstream genes, accompanied by cytochromes P450 (CYPs), antioxidative genes, and heat shock proteins (HSPs). Our results are consistent with transcriptome detection, indicating that ammonia has a negative effect on immune responses and causes inflammatory injury of the trachea. This study provided baseline information for exploration of the molecular mechanism of NH3-PM2.5 induced respiratory diseases.

Trend analysis and forecast of PM2.5 in Fuzhou, China using the ARIMA model

Atmospheric haze from particulate matter has become a major health concern in many large and medium-sized cities of China. Fine airborne particles, PM2.5, are a major component of haze, and the reasons for their fluctuating concentrations need to be better understood to improve air quality. While much of the air quality data from the China National Environmental Monitoring Centre (CNEMC) has already been analyzed for larger Chinese cities such as Beijing and Shanghai, the data from Fuzhou is the topic of this study. Time series data of pollutant concentrations and meteorological parameters were accessed for Fuzhou from August 2014 to July 2016, covering two cold periods (November through February) and two warm periods (May through July). PM2.5 concentrations were compared with those of other pollutants and with meteorological parameters, and the AutoRegressive Integrated Moving Average (ARIMA) model was applied to forecast PM2.5 concentrations. The correlation analysis of Spearman’s rho showed that PM2.5 concentrations had significant positive correlations with PM10, SO2 and NO2 concentrations, while they had negative correlations with meteorological parameters. The ARIMA results showed that PM2.5 concentrations experienced seasonal fluctuations over the two years, which were higher in the cold periods and lower in the corresponding warm periods, ranging from 23 to 52 μg/m3 and from 19 to 31 μg/m3, respectively. Average PM2.5 concentrations during these same cold periods and warm periods were 35 μg/m3 over the two cold periods, which was 52% higher than the 23 μg/m3 averaged over the two warm periods. Compared to the two observed years, 2014–15 and 2015–16, the forecast of PM2.5 concentrations for 2016–17, ranging from about 15 to 30 μg/m3, exhibited similar seasonal fluctuations but also reduced in the same period, ranging from 19 to 52 μg/m3. It is speculated that the Fuzhou Government's macro-policy on air quality may be one of the reasons for the decrease in PM2.5 concentration during the cold periods. Possible causes were given for the discrepancies between actual and fit values.

Image Dehazing and Enhancement Using Principal Component Analysis and Modified Haze Features

This paper presents a computationally efficient haze removal and image enhancement methods. The major contribution of the proposed research is two-fold: (i) an accurate atmospheric light estimation using principal component analysis, and (ii) learning-based transmission estimation. To reduce the computational cost, we impose a constraint on the candidate pixels to estimate the haze components in the sub-image. In addition, the proposed method extracts modified haze-relevant features to estimate an accurate transmission using random forest. Experimental results show that the proposed method can provide high-quality results with a significantly reduced computational load compared with existing methods. In addition, we demonstrate that the proposed method can significantly enhance the contrast of low-light images according to the assumption on the visual similarity between the inverted low-light and haze images.

Origin of Fine Particulate Carbon in the Rural United States.

Carbonaceous compounds are a significant component of fine particulate matter and haze in national parks and wilderness areas where visibility is protected, i.e., class I areas (CIAs). The Regional Haze Rule set the goal of returning visibility in CIAs on the most anthropogenically impaired days to natural by 2064. To achieve this goal, we need to understand contributions of natural and anthropogenic sources to the total fine particulate carbon (TC). A Lagrangian chemical transport model was used to simulate the 2006-2008 contributions from various source types to measured TC in CIAs and other rural lands. These initial results were incorporated into a hybrid model to reduce systematic biases. During summer months, fires and vegetation-derived secondary organic carbon together often accounted for >75% of TC. Smaller contributions, <20%, from area and mobile sources also occurred. During the winter, contributions from area and mobile sources increased, with area sources accounting for half or more of the TC in many regions. The area emissions were likely primarily from residential and industrial wood combustion. Different fire seasons were evident, with the largest contributions during the summer when wildfires occur and smaller contributions during the spring and fall when prescribed and agricultural fires regularly occur.

On the Seasonality of Arctic Black Carbon

Arctic haze has a distinct seasonal cycle with peak concentrations in winter but pristine conditions in summer. It is demonstrated that the Geophysical Fluid Dynamics Laboratory (GFDL) atmospheric general circulation model (AM3) can reproduce the observed seasonality of Arctic black carbon (BC), an important component of Arctic haze. The model is used to study how large-scale circulation and removal drive the seasonal cycle of Arctic BC. It is found that despite large seasonal shifts in the general circulation pattern, the transport of BC into the Arctic varies little throughout the year. The seasonal cycle of Arctic BC is attributed mostly to variations in the controlling factors of wet removal, namely the hydrophilic fraction of BC and wet deposition efficiency of hydrophilic BC. Specifically, a confluence of low hydrophilic fraction and weak wet deposition, owing to slower aging process and less efficient mixed-phase cloud scavenging, respectively, is responsible for the wintertime peak of BC. The transition to low BC in summer is the consequence of a gradual increase in the wet deposition efficiency, whereas the increase of BC in late fall can be explained by a sharp decrease in the hydrophilic fraction. The results presented here suggest that future changes in the aging and wet deposition processes can potentially alter the concentrations of Arctic aerosols and their climate effects.

Long-term trend and spatiotemporal variations of haze over China by satellite observations from 1979 to 2013

With the fast development of economy and industry in the past thirty years, many large cities in the eastern and southwestern areas of China are experiencing increased haze events and atmospheric pollution, which causes significant impacts on the regional environment, human health, and even climate. The long-term trend and spatiotemporal variations of haze over China during recent 30 years are investigated using TOMS AAI products. In addition, the heavy haze events that occurred in January 2013 over eastern China are explored using AAI products from TOU on board FY-3A. Validation results show that satellite AAI products can be used for haze monitoring since it is sensitive to the carbonaceous aerosol, which is one of the main components of haze. In China, the high AAI values (>1.0) mainly located in the main four areas with intense anthropogenic activities, except for the desert region in Northwestern China. In the eastern and northeastern region, AAI peaks dominate in spring before 2005 since those areas were always affected by dust in spring. However, after 2005, AAI peaks appear in winter over eastern China because of haze. Moreover, in the northeastern region, AAI peaks dominate in winter with a secondary peak in spring because this area is affected by both dust and haze. In the southern region, the AAI peaks always dominate in spring since the high-level air pollution often occur in spring, but a decreasing trend is acquired during recent ten years. Over eastern China and northeastern China, AAI shows an increasing trend during recent 30 years in winter, which reveals that the haze over these areas is strengthen. A case study result shows that the heavy haze events occurred in January 2013 in eastern China can be clearly identified from the AAI products of TOU/FY-3A. The daily coverage area with AAI > 3.0 peaks at five periods at this time, i.e. Jan. 7–8, Jan. 13, Jan. 18, Jan. 23, and, Jan. 28–29, which agrees well with the haze events recorded by in-situ measurements.

Impact of human activities on haze in Beijing based on grey relational analysis

Based on the grey relational analysis of haze components and the factors associated with socio-economic development in Beijing, this paper found that social development activities, such as energy consumption per GDP unit, urban road construction and urban greening, have great impact on haze, including both promotion and inhibition impacts. It should be noticed that human activities which gained much attention before, such as ownership of motor vehicles and residential energy consumption, do not have a strong effect on haze according to our study, which is contrary to previous studies and government reports.

Spatiotemporal analysis of ambient air pollution exposure and respiratory infections cases in Beijing.

Ambient air pollutants (PM2.5) are components of persistent haze in Beijing during the autumn and winter seasons. We collected hourly PM2.5 monitoring data for 35 days from 35 sites in Beijing during 2012. We also identified patients developing respiratory infections during the same time period in the same locale. A BME model was used to simulate environmental exposure concentrations over the course of each day. A medical accessibility analysis was performed to exclude the impact of medical availability on the analysis. A spatial analysis was included in the evaluation of the relationship between exposure duration and concentration of PM2.5 with the development of acute respiratory disease. A low concentration of PM2.5 (greater than 35 µg/m3 and less than 115 µg/m3) for at least 3 days was associated with an increased risk of acute respiratory disease. A high concentration of PM2.5 (greater than 115 µg/m3) was associated with an increased risk of infection even after 1 day of exposure.

Viewing direction measurements on flat and curved flexible E‐paper displays

AbstractThe viewing direction characterization of reflective displays is more demanding than for emissive displays because defined illumination–detection geometries must be maintained for each viewing direction. In addition, the geometry should mimic the behavior of viewers who tend to exclude the reflection of light sources from a handheld reflective display. Viewing direction data was extracted from measured Bidirectional Reflectance Distribution Functions, showing that a source inclination of 45° sufficiently excludes the specular and haze components of source reflection from the measurement. Applying this so‐called 45/θ geometry to curved flexible displays is not straightforward since viewing direction, display curvature, and alignment each affect the measured reflectance. The viewing direction geometry proposed for convex cylindrical displays uses a ring light to deliver viewing‐direction independent illuminance, and maps the range of viewing directions onto the pixels of an image sensor. The illumination can also be applied to flat displays, allowing direct comparisons of flat and cylindrical display states. First results on e‐paper show good agreement with Bidirectional Reflectance Distribution Function data.

Source identification of short-lived air pollutants in the Arctic using statistical analysis of measurement data and particle dispersion model output

Abstract. As a part of the IPY project POLARCAT (Polar Study using Aircraft, Remote Sensing, Surface Measurements and Models, of Climate Chemistry, Aerosols and Transport), this paper studies the sources of equivalent black carbon (EBC), sulphate, light-scattering aerosols and ozone measured at the Arctic stations Zeppelin, Alert, Barrow and Summit during the years 2000–2007. These species are important pollutants and climate forcing agents, and sulphate and EBC are main components of Arctic haze. To determine where these substances originate, the measurement data were combined with calculations using FLEXPART, a Lagrangian particle dispersion model. The climatology of atmospheric transport from surrounding regions on a twenty-day time scale modelled by FLEXPART shows that the stations Zeppelin, Alert and Barrow are highly sensitive to surface emissions in the Arctic and to emissions in high-latitude Eurasia in winter. Emission sensitivities over southern Asia and southern North America are small throughout the year. The high-altitude station Summit is an order of magnitude less sensitive to surface emissions in the Arctic whereas emissions in the southern parts of the Northern Hemisphere continents are more influential relative to the other stations. Our results show that for EBC and sulphate measured at Zeppelin, Alert and Barrow, northern Eurasia is the dominant source region. For sulphate, Eastern Europe and the metal smelting industry in Norilsk are particularly important. For EBC, boreal forest fires also contribute in summer. No evidence for any substantial contribution to EBC from sources in southern Asia is found. European air masses are associated with low ozone concentrations in winter due to titration by nitric oxides, but are associated with high ozone concentrations in summer due to photochemical ozone formation. There is also a strong influence of ozone depletion events in the Arctic boundary layer on measured ozone concentrations in spring and summer. These results will be useful for developing emission reduction strategies for the Arctic.

Ammonia emissions from dairy production in Wisconsin

Ammonia gas is the only significant basic gas that neutralizes atmospheric acid gases produced from combustion of fossil fuels. This reaction produces an aerosol that is a component of atmospheric haze, is implicated in nitrogen (N) deposition, and may be a potential human health hazard. Because of the potential impact of NH3 emissions, environmentally and economically, the objective of this study was to obtain representative and accurate NH3 emissions data from large dairy farms (>800 cows) in Wisconsin. Ammonia concentrations and climatic measurements were made on 3 dairy farms during winter, summer, and autumn to calculate emissions using an inverse-dispersion analysis technique. These study farms were confinement systems utilizing freestall housing with nearby sand separators and lagoons for waste management. Emissions were calculated from the whole farm including the barns and any waste management components (lagoons and sand separators), and from these components alone when possible. During winter, the lagoons’ NH3 emissions were very low and not measurable. During autumn and summer, whole-farm emissions were significantly larger than during winter, with about two-thirds of the total emissions originating from the waste management systems. The mean whole-farm NH3 emissions in winter, autumn, and summer were 1.5, 7.5, and 13.7% of feed N inputs emitted as NH3-N, respectively. Average annual emission comparisons on a unit basis between the 3 farms were similar at 7.0, 7.5, and 8.4% of input feed N emitted as NH3-N, with an annual average for all 3 farms of 7.6±1.5%. These winter, summer, autumn, and average annual NH3 emissions are considerably smaller than currently used estimates for dairy farms, and smaller than emissions from other types of animal-feeding operations.

Characterization of natural haze protein in sauvignon white wine

The natural precipitate of a white Sauvignon wine was dissolved, lyophilized and analyzed by molecular exclusion FPLC and/or electrophoresis (SDS–PAGE, native and isoelectrofocusing) using three types of stains (Coomassie blue, silver stain and PAS stain). The protein spots were digested and analyzed by MALDI-TOF/TOF MS. Direct analysis of the precipitates by SDS–PAGE gave no bands with Coomassie blue stain and five bands with silver stain. It was not possible to identify these bands by MALDI-TOF/TOF MS. However, applying molecular exclusion FPLC prior to SDS–PAGE improved sensitivity and enabled three bands to be identified. These were the previously described thaumatin-like protein (VVTL1) and two proteins that to our knowledge have never been described as components of protein haze: β-(1-3)-glucanase and ripening-related protein grip22 precursor.

Mask Haze Measurement by Spectroscopic Ellipsometry

Haze formation on a reticle continues to be a major problem for the semiconductor industry. Haze can be formed on the outside pellicle and on the quartz backside of the reticle. The major component of haze is known to be ammonium sulfate that comes from the reticle cleaning process. Reticle materials, exposure wavelength, the roughness of a photomask and haze affect resolution and process latitude. Thus, haze on a mask surface needs to be studied. We need to know the usable lifetime of the reticle in terms of haze formation and how to prolong reticle lifetime by removing haze, if possible. In this paper, we introduce the haze measurement method by spectroscopic ellipsometry. The quantity of haze including the roughness of the reticle can be accurately measured by spectroscopic ellipsometry. Spectroscopic data shows an increase in the measured ellipsometry value with energy dose given to the reticle. We confirm that this signal increase is a direct result of the increase in haze quantity with exposed dose.