SO2 Data Research Articles

Long-term trend analysis of criteria pollutants in megacity of Delhi: Failure or success of control policies

The study aimed to analyze the long-term trend of four criteria pollutants (PM2.5 and NO2 during 2006–2019; SO2 and CO during 2000–2019) and the effect of pollution control measures in Delhi. The hourly data of PM2.5 and NO2 was taken from six air quality monitoring stations while SO2 and CO data was retrieved from MERRA reanalysis (Modern-Era Retrospective Analysis for Research and Applications) products. PM2.5 and NO2 datasets from six monitoring stations were divided into three categories based on their years of data collection. At all the categories of sites, both the pollutants showed three distinct trends including a decreasing trend which was common from 2015 to 2019. The annual average trends of SO2 and CO showed two distinct trends including an increasing trend which was common from 2013 to 2019. The study analyzed various control policies, among them Compressed Natural Gases (CNG) implementation in December 2002, is a historic measure which was followed by 13.5% decrease in particulate matter trend and 20% increase in NO2 trend during 2002–2005 while 7% decrease in CO trend during 2003–2005. The introduction of ultralow sulfur diesel and closure of coal based thermal power plants reduced SO2 levels significantly during 2011–2012.

Highlighting the role of earth observation Sentinel5P TROPOMI in monitoring volcanic eruptions: a report on Hunga Tonga, a Submarine Volcano

ABSTRACT The present study utilized earth observation Sentinel5P TROPOMI (TROPOspheric Monitoring Instrument) data for monitoring submarine volcanic activities. The world has seen a sequence of abrupt volcanic eruptions within one-month duration i.e., on 20th December 2021, 13th January 2022 and 15th January 2022. This caused the emissions of natural gaseous pollutants such as Sulphur Dioxide (SO2), Hydrogen Sulphide (H2S) and ash particles. Atmospheric SO2, AAI (Absorbing Aerosol Index) and Cloud fraction data are retrieved from the TROPOMI onboard the Copernicus Sentinel5P European Space Agency satellite. Results confirmed the increased concentration of SO2 column density after 20th December, and it was observed to be 0.002 mol/m2, the highest concentration during the period of study. Generally, SO2 concentration stays within 0.001 mol/m2 or less. Concentration was seen increasing after the first eruption and reached maximum after 15th January 2022 eruption to a level up to 0.002 mol/m2. Similar outcomes were observed for AAI for the chosen site. AAI values increased to 25,000 fold (AAI values of 1.25) during the first eruption and increased to 29,000 fold on 15th January 2022 after eruptions (AAI values of 1.45) as compared AAI values of 0.005 or negative AAI values before eruptions. A cloud fraction of value 1 is observed during this time, along with the top most cloud height at 14,233.16 m (14.23 kms) above the crater of the volcano (maximum estimation limit by S5P). These values are higher as compared to isolated islands, as there are no other sources of emissions at surrounding environment. The outcomes support the role of Earth Observation datasets for disaster monitoring and management plans for mitigation.

The Impact of Economic Development of the Guangdong-Hongkong-Macao Greater Bay Area on Air Pollution: Investigation Based on Remote Sensing Data of Nighttime Lights and Air Pollution

Whether the Guangdong-Hongkong-Macao Greater Bay Area (GBA) can achieve the goal of environmental protection at the same time as its rapid economic development is an important issue that deserves attention. In this paper, we obtained remote sensing data on air pollution including CO, NO2, SO2, PM10, PM2.5, and nighttime light data. We combined the remote sensing data with county-level economic statistics to study economic development and air pollution in the Guangdong-Hongkong-Macao GBA. Our research showed that the economic development in the Guangdong-Hongkong-Macao GBA significantly increased the emissions of NO2, PM10, and PM2.5 and did not have significant effects on CO and SO2. We constructed an emission intensity indicator of pollutants per unit of luminance. We found that the unit emission intensity of four categories of pollutants, PM10, PM2.5, SO2, and CO, showed a significant decreasing trend in line with economic development. The emission intensity of NO2 showed a trend of first decreasing and then increasing. The conclusion of this research illustrates that the emission intensity of pollutants per unit of luminosity in the Guangdong-Hongkong-Macao GBA is continuously reducing. The emission of pollutants will gradually change from slow growth to zero growth. However, the emission of NO2 pollution should be an area of focus in achieving the dual goals of economic development and environmental protection.

The Binomial–Natural Discrete Lindley Distribution: Properties and Application to Count Data

In this paper, a new discrete distribution called Binomial–Natural Discrete Lindley distribution is proposed by compounding the binomial and natural discrete Lindley distributions. Some properties of the distribution are discussed including the moment-generating function, moments and hazard rate function. Estimation of the distribution’s parameter is studied by methods of moments, proportions and maximum likelihood. A simulation study is performed to compare the performance of the different estimates in terms of bias and mean square error. SO2 data applications are also presented to see that the new distribution is useful in modeling data.

Spatial-temporal Distribution and Evolution Characteristics of Air Pollution in Beijing-Tianjin-Hebei Region Based on Long-term "Ground-Satellite" Data

This study aimed to promote the coordinated development of regional social economy and ecological environment, build a better living environment, accurately prevent and control pollution, and carry out in-depth surveys and general surveys of air pollution in Beijing, Tianjin, and Hebei. Based on 6 years (June 2014 to December 2019) of ground environmental observation data and satellite data from 2000 to 2019, the distribution characteristics and evolution trend of air pollution in different time and spatial scales were analyzed. The results showed that:① according to the daily average concentration of PM2.5 at the sites, the pollution in the Beijing-Tianjin-Hebei region showed the characteristics of more days, heavy levels, and overall improvement. Pollution mainly occurred from October to April of the following year, accounting for nearly half a year. The pollution level of PM2.5 was the best at Zhangjiakou, followed by Qinhuangdao. ② Based on the 20-year average PM2.5 annual average concentration data retrieved from satellites, the PM2.5 concentration presented a spatial distribution characteristic in which that in the plains was higher than that in mountain area, and PM2.5 concentration in the city was higher than that in the suburbs. PM2.5 concentration changed with time, showing a four-stage bimodal structure of "M"-type evolution characteristics, which gradually increased starting in 2000; the first peak appeared in 2006 and gradually decreased from 2007 to 2012. It rose sharply to the second peak in 2013 and then decreased yearly until 2017. ③ The monthly average AOT data based on satellites every 10 years indicated that the value of AOT in the first time period (2000-2009) was larger than that in the same month of the second time period (2010-2019). The maximum value was in July, and the minimum value was in December. The monthly average AOT in Zhangjiakou and Chengde changed slightly over the past 20 years, and the seasonal and spatial differences were significant in the plain area. ④ Judging from the daily average value of O3-8h observed at the stations, good levels of O3-8h concentrations in the Beijing-Tianjin-Hebei area occurred frequently and widely from March to October. There were at least seven instances of light pollution levels, and the moderate pollution levels and above were not observed. ⑤ The daily average value of SO2 observed on the ground showed that there was no light pollution or above; the good pollution level occurred in winter, and most appeared in the form of pollution for several consecutive days. ⑥ The analysis of AQI data revealed that from 2015 to 2019, the proportion of AQI excellent grades in Beijing increased from 27% to 38%, and the proportion of Tianjin AQI good grades increased from 44% to 64%. The highest proportion of Handan AQI superior grades appeared in 2016, accounting for only 9%. ⑦ The 20-year monthly average concentration of SO2 data based on satellites showed that high-value areas were in Handan, Xingtai, and Shijiazhuang, and low-value areas were in Zhangjiakou and Chengde. The 20-year average NO2 data showed that the high-value centers were in Beijing, Tianjin, Tangshan, Handan, Xingtai, and Shijiazhuang.

Association Between Sulfur Dioxide and Daily Inpatient Visits With Respiratory Diseases in Ganzhou, China: A Time Series Study Based on Hospital Data.

BackgroundSulfur dioxide (SO2) has been reported to be related to the mortality of respiratory diseases, but the relationship between SO2 and hospital inpatient visits with respiratory diseases and the potential impact of different seasons on this relationship is still unclear.MethodsThe daily average concentrations of air pollutants, including SO2 and meteorological data in Ganzhou, China, from 2017 to 2019 were collected. The data on daily hospitalization for respiratory diseases from the biggest hospital in the city were extracted. The generalized additive models (GAM) and the distributed lag non-linear model (DLNM) were employed to evaluate the association between ambient SO2 and daily inpatient visits for respiratory diseases. Stratified analyses by gender, age, and season were performed to find their potential effects on this association.ResultsThere is a positive exposure-response relationship between SO2 concentration and relative risk of respiratory inpatient visits. Every 10 μg/m3 increase in SO2 was related to a 3.2% (95% CI: 0.6–6.7%) exaltation in daily respiratory inpatient visits at lag3. In addition, SO2 had a stronger association with respiratory inpatient visits in women, older adults (≥65 years), and warmer season (May-Oct) subgroups. The relationship between SO2 and inpatient visits for respiratory diseases was robust after adjusting for other air pollutants, including PM10, NO2, O3, and CO.ConclusionThis time-series study showed that there is a positive association between short-term SO2 exposure and daily respiratory inpatient visits. These results are important for local administrators to formulate environmental public health policies.

A data-augmentation approach to deriving long-term surface SO2 across Northern China: Implications for interpretable machine learning

Until recently, Northern China was one of the most SO2 polluted regions in the world. The lack of long-term and spatially resolved surface SO2 data hinders retrospective evaluation of relevant environmental policies and human health effects. This study aims to derive the spatiotemporal distribution of surface SO2 across Northern China during 2005–2019. As “concept drift” causes substantial estimation bias in back-extrapolation, we propose a new approach named the robust back-extrapolation via data augmentation approach (RBE-DA) to model the long-term surface SO2. The results show that the population-weighted regional SO2 ([SO2]pw) increased from 2005 to 2007 and decreased steadily afterwards. The [SO2]pw decreased by 80.4% from 74.2 ± 28.4 μg/m3 in 2007 to 14.6 ± 4.8 μg/m3 in 2019. The predicted spatial distributions for each year show that the SO2 pollution was severe (more than 20 μg/m3) in most areas of Northern China until 2017. By using model interpretation methods, we visually reveal the mechanism of estimation bias in the back-extrapolation. Specifically, the training data is severely imbalanced with respect to the satellite-retrieved SO2 column densities (i.e., it is short on high-value samples), so the benchmark model is unable to extrapolate the effects of this important predictor. This study provides long-term surface SO2 data for post hoc evaluation and human exposure assessment in Northern China, while demonstrating that the interpretable machine learning approach is critical for model diagnostics and refinement. Leveraging satellite retrievals, the RBE-DA approach can be applied worldwide to back-extrapolate various measures of air quality.

Spatial and temporal changes analysis of air quality before and after the COVID-19 in Shandong Province, China.

Due to the COVID-19 pandemic outbreak, the home quarantine policy was implemented to control the spread of the pandemic, which may have a positive impact on the improvement of air quality in China. In this study, Google Earth Engine (GEE) cloud computing platform was used to obtain CO, NO2, SO2 and aerosol optical depth (AOD) data from December 2018-March 2019, December 2019-March 2020, and December 2020-March 2021 in Shandong Province. These data were used to study the spatial and temporal distribution of air quality changes in Shandong Province before and after the pandemic and to analyze the reasons for the changes. The results show that: (1) Compared with the same period, CO and NO2 showed a decreasing trend from December 2019 to March 2020, with an average total change of 4082.36 mol/m2 and 167.25 mol/m2, and an average total change rate of 4.80% and 38.11%, respectively. SO2 did not have a significant decrease. This is inextricably linked to the reduction of human travel production activities with the implementation of the home quarantine policy. (2) The spatial and temporal variation of AOD was similar to that of pollutants, but showed a significant increase in January 2020, with an average total amount increase of 1.69 × 107 up about 2.54% from December 2019 to March 2020. This is attributed to urban heating and the reduction of pollutants such as NOx. (3) Pollutants and AOD were significantly correlated with meteorological data (e.g., average temperature, average humidity, average wind speed, average precipitation, etc.). This study provides data support for atmospheric protection and air quality monitoring in Shandong Province, as well as theoretical basis and technical guidance for policy formulation and urban planning.

Lava Volume from Remote Sensing Data: Comparisons with Reverse Petrological Approaches for Two Types of Effusive Eruption

Five effusive eruptions of Piton de la Fournaise (La Réunion) are analyzed to investigate temporal trends of erupted mass and sulfur dioxide (SO2) emissions. Daily SO2 emissions are acquired from three ultraviolet (UV) satellite instruments (the Ozone Monitoring Instrument (OMI), the Ozone Mapping and Profiler Suite (OMPS), and the Tropospheric Monitoring Instrument (TROPOMI)) and an array of ground-based UV spectrometers (Network for Observation of Volcanic and Atmospheric Change (NOVAC)). Time-averaged lava discharge rates (TADRs) are obtained from two automatic satellite-based hot spot detection systems: MIROVA and MODVOLC. Assuming that the lava volumes measured in the field are accurate, the MIROVA system gave the best estimation of erupted volume among the methods investigated. We use a reverse petrological method to constrain pre-eruptive magmatic sulfur contents based on observed SO2 emissions and lava volumes. We also show that a direct petrological approach using SO2 data might be a viable alternative for TADR estimation during cloudy weather that compromises hot spot detection. In several eruptions we observed a terminal increase in TADR and SO2 emissions after initial emission of evolved degassed magma. We ascribe this to input of deeper, volatile-rich magma into the plumbing system towards the end of these eruptions. Furthermore, we find no evidence of volatile excess in the five eruptions studied, which were thus mostly fed by shallow degassed magma.

Assessment of the Performance of TROPOMI NO2 and SO2 Data Products in the North China Plain: Comparison, Correction and Application

The TROPOspheric Monitoring Instrument (TROPOMI) aboard the Sentinel-5 Precursor satellite has been used to detect the atmospheric environment since 2017, and it is of great significance to investigate the accuracy of its products. In this work, we present comparisons between TROPOMI tropospheric NO2 and total SO2 products against ground-based MAX-DOAS at a single site (Xianghe) and OMI products over a seriously polluted region (North China Plain, NCP) in China. The results show that both NO2 and SO2 data from three datasets exhibit a similar tendency and seasonality. In addition, TROPOMI tropospheric NO2 columns are generally underestimated compared with collocated MAX-DOAS and OMI data by about 30–60%. In contrast to NO2, the monthly average SO2 retrieved from TROPOMI is larger than MAX-DOAS and OMI, with a mean bias of 2.41 (153.8%) and 2.17 × 1016 molec cm−2 (120.7%), respectively. All the results demonstrated that the TROPOMI NO2 as well as the SO2 algorithms need to be further improved. Thus, to ensure reliable analysis in NCP area, a correction method has been proposed and applied to TROPOMI Level 3 data. The revised datasets agree reasonably well with OMI observations (R > 0.95 for NO2, and R > 0.85 for SO2) over the NCP region and have smaller mean biases with MAX-DOAS. In the application during COVID-19 pandemic, it showed that the NO2 column in January-April 2020 decreased by almost 25–45% compared to the same period in 2019 due to the lockdown for COVID-19, and there was an apparent rebound of nearly 15–50% during 2021. In contrast, a marginal change of the corresponding SO2 is revealed in the NCP region. It signifies that short-term control measures are expected to have more effects on NO2 reduction than SO2; conversely, we need to recognize that although the COVID-19 lockdown measures improved air quality in the short term, the pollution status will rebound to its previous level once industrial and human activities return to normal.

A Study on the Concentrations Calibration for NO, NO2, SO2, CO and VOC Sensors Reflecting the Influence of Temperature and Relative Humidity

The objectives of this study is to evaluate the effects of temperature and relative humidity on electrochemical and pothoionization sensors used to measure air pollutants (NO, NO2, SO2, CO, VOC), and to propose calibration methods to produce more reliable monitoring data. Measurement sensors for each substance were tested under four temperatures and three relative humidity conditions. The linearity measured for each condition of temperature and relative humidity was excellent, and the coefficient of determination of 0.92 to 0.99 was shown in all sensors. Under the condition that the concentration was zero, the baseline (intercept) of zero increased as the relative humidity of NO and some VOC sensors increased. As the temperature increased, the gas concentration increase rate (slope) in accordance with the variation of the SO2 and VOCM sensors increased, and the intercept of the CO sensor also tended to increase. In this study, slope and intercept were used to calibrate sensors for each air pollutant, and it is expected that the reliability of NO, NO2, SO2, CO, and VOC sensor data can be improved by deriving a relational expression based on this paper.

Densities and viscosities of, and solubilities of acidic gases (SO2 and H2S) in natural deep eutectic solvents

Natural deep eutectic solvents (NADESs) are a subcategory of deep eutectic solvents (DESs) consisting solely of natural compounds. They can replace existing DESs that contain toxic compounds and are difficult to dispose of in many applications. However, the detailed physical properties data of NADESs are still very scarce in the literature. In this work, a series of NADESs with choline chloride (ChCl) as the shared hydrogen-bond acceptor (HBA), and five dicarboxylic acids (oxalic, malonic, maleic, malic and tartaric) as the varied hydrogen-bond donors (HBDs) were prepared. The densities and viscosities of prepared NADESs were determined from 293.2 to 353.2 K at 1.013 bar. The densities and viscosities data were fitted using empirical equations. An important application of DESs is gas separation, which requires knowing the solubilities of gases in DESs. Therefore, a NADES with relatively good fluidity was selected out as a representative to determine the solubilities of two typical acidic gases (SO2 and H2S) in it. The solubilities of SO2 and H2S were determined from 298.2 to 333.2 K and from 0 to 1 bar. The SO2 and H2S solubilities data were fitted using the Krichevsky − Kasarnovsky equation.

Retraction Note: Air pollution detection based on image recognition and evaluation of measles prevention and immunity effect

This article first studies the image recognition method and some of its current problems and promotes the comparison and selection of existing image recognition algorithms. The article introduces in detail the training principle and process of the restricted Boltzmann machine and the combination of the restricted Boltzmann machine. Based on this point, this paper selects the NO2, SO2, CO, PM2.5, PM10, and other air pollution data of 161 prefecture-level cities monitored by the Ministry of Environment in 2020 and compares the data of 161 prefecture-level cities in 2020. The urban air quality reaches the level to be tested. Analyze air pollution status and detection methods and seasonal and monthly characteristics of pollution (or annual air pollution change characteristics), as well as comparative analysis between regions and differences in air pollution detection between different cities, explaining the impacts on air pollution in different regions of China. This is the main reason for the change. Finally, this article investigates and studies the epidemiological nature and risk factors of measles in S city and the impact of immune effects on measles prevention, to determine the distribution and characteristics of measles in S city, analyze disease risks, and take preventive measures to prevent more reasonably and control measles. Prior to this, a plan was made to provide a reasonable theoretical basis. At the same time, measles antibody testing and related research will be conducted on some healthy permanent residents in city S to understand the immune effect of the floating population and the factors affecting the immune effect, so as to further strengthen the epidemic prevention of the floating population, which laid the foundation for the work of crowd vaccination. This article has realized the rapid development of measles prevention technology by evaluating the effectiveness of measles prevention and resistance based on image recognition and application of air pollution detection results.

Does apparent temperature modify the effects of air pollution on respiratory disease hospital admissions in an industrial area of South Africa?

Background: Temperature and air pollution are often treated as separate risk factors and very few studies have investigated effect modification by temperature on air pollution, and the impact of this interaction on human health in Africa. This study therefore investigated the modifying effects of temperature on the association between air pollution and Respiratory disease (RD) hospital admission in South Africa.
 Methods: RD admission data (ICD10 J00-J99) were obtained from two hospitals located in Secunda, South Africa beween 1 January 2011 to 31 October 2016. NO2, SO2, PM10, PM2.5, temperature and relative humidity data were obtained from the South African Weather Services. A case-crossover epidemiological study design was applied and lag0-1 was used. Models were adjusted for public holidays and Apparent Temperature (Tapp). Tapp was classified as warm (Tapp>75th percentile), cold (Tapp<25th percentile) and normal (Tapp 25th-75th percentile).
 Results: Of the 14 568 RD admissions, approximately equal number of females and males were admitted. The average daily NO2, SO2, PM2.5 and PM10 levels were 12.4 μm/m3, 8.5 μm/m3, 32.3 μm/m3 and 68.6 μm/m3, respectively. Overall, a 10 μm/m3 increase in SO2 on warm days was associated with an increase in RD hospital admissions among the patients by 8.5% (95% Conf. Int: 0.4%, 17.2%) and 8.4% (95% Conf. Int: 0.3%, 17.1%) after adjustment for PM2.5 and PM10 respectively. However, increasing PM2.5 or PM10 by 10 μm/m3 was associated with an increase in RD hospital admissions when the temperature was normal after adjusting for SO2. On cold days there were significant associations between the SO2 and RD admissions among the 0-14 year age group after adjusting for either PM2.5 (6.5%; 95% Conf.Int: 0.9%, 12.4%) or PM10 (5.5%; 95% Conf.Int: 0.3%, 11.1%).
 Conclusions: SO2 was affected by extremes of temperature while the particulate matters had effect on RD admission during normal temperature in Secunda.

Spatial Distribution Pattern and Influencing Factors of Industrial Pollution Emissions in Yangtze River Economic Belt

The development of Yangtze River economic belt emphasizes the strategy of "no large-scale development, joint efforts in large-scale protection". The restoration of the Yangtze River environment has become an important part of the development of the region. Therefore, it is important to explore the spatial distribution of industrial pollution emissions and its influencing factors in the Yangtze River economic belt. Using industrial SO2, wastewater, and soot emission data from 102 cities in the Yangtze River economic belt, from 2013 to 2017, this study analyzes the spatial distribution of industrial pollution emissions in the Yangtze River economic belt. This was achieved using spatial autocorrelation analysis and Getis-Ord Gi* methods, and by adopting the logarithmic mean Divisia index model to decompose the influencing factors of industrial pollution emissions in the Yangtze River economic belt. The results show that from 2013 to 2017, the emissions of industrial SO2, wastewater, and soot in the Yangtze River economic belt exhibited a downward trend, with a decrease in the number of high-emission cities and an increase in the number of low-emission cities. The spatial correlation of industrial pollutants increased with time, and technological improvements had the most significant inhibitory effect, making it the most important factor affecting industrial pollution emissions in the Yangtze River economic belt.

RETRACTED ARTICLE: Air pollution detection based on image recognition and evaluation of measles prevention and immunity effect

This article first studies the image recognition method and some of its current problems and promotes the comparison and selection of existing image recognition algorithms. The article introduces in detail the training principle and process of the restricted Boltzmann machine and the combination of the restricted Boltzmann machine. Based on this point, this paper selects the NO2, SO2, CO, PM2.5, PM10, and other air pollution data of 161 prefecture-level cities monitored by the Ministry of Environment in 2020 and compares the data of 161 prefecture-level cities in 2020. The urban air quality reaches the level to be tested. Analyze air pollution status and detection methods and seasonal and monthly characteristics of pollution (or annual air pollution change characteristics), as well as comparative analysis between regions and differences in air pollution detection between different cities, explaining the impacts on air pollution in different regions of China. This is the main reason for the change. Finally, this article investigates and studies the epidemiological nature and risk factors of measles in S city and the impact of immune effects on measles prevention, to determine the distribution and characteristics of measles in S city, analyze disease risks, and take preventive measures to prevent more reasonably and control measles. Prior to this, a plan was made to provide a reasonable theoretical basis. At the same time, measles antibody testing and related research will be conducted on some healthy permanent residents in city S to understand the immune effect of the floating population and the factors affecting the immune effect, so as to further strengthen the epidemic prevention of the floating population, which laid the foundation for the work of crowd vaccination. This article has realized the rapid development of measles prevention technology by evaluating the effectiveness of measles prevention and resistance based on image recognition and application of air pollution detection results.

An analysis of volcanic SO2 and ash emissions from Copahue volcano

During 2016, Copahue volcano (Neuquén, Argentina) made several eruptions and emitted ash and gases. This paper presents an analysis of the volcanic plume using dispersion models, remote sensor data, and analysis of sedimentary ash collected at the Caviahue Health Center from early October to 21st December 2016. Water-soluble elements contained in volcanic ash leachates were quantified. We have carried out a qualitative comparison of HYSPLIT dispersion simulations with SO2 and aerosol data obtained from OMI and OMPS sensor on board of AURA and SUOMI NPP satellites, as well as between modeled concentrations and sampled surface sediments. Good agreement was observed between gas dispersion plumes from HYSPLIT and total column SO2 values, as well as between volcanic ash plumes and satellite aerosol indices.The average SO2 emission rate in the analyzed events was 985.7 ± 492.9 t/d and the average emission rate of ash 2.6 103 kt/d (2.9 104 kg/s). In situ observations of ash deposition rates peaked at 0.23 g/h on 30th November. The average measured deposition rate from 1st October to 21st December was 0.12 g/h, consistent with modeled values. The geochemical analysis of ash leachate showed major content of sodium (Na), calcium (Ca) and magnesium (Mg) with 207.8 mg/kg, 209.5 mg/kg, and 195.3 mg/kg, respectively. Concentrations sulfate, chlorides, and fluoride anions were 990.7 mg/kg, 352.34 mg/kg, and 129.15 mg/kg, respectively. The SO2/HCl molar ratio was 1.05. In addition, traces of As, Cr, Hg, Mo, Pb, and Se were detected in concentrations ranging from 0.2 mg/kg to 6.0 mg/kg.

The Association between Preterm Birth and Ambient Air Pollution Exposure in Shiyan, China, 2015-2017.

Shortening of the gestational duration has been found associated with ambient air pollution exposure. However, the critical exposure windows of ambient air pollution for gestational duration remain inconsistent, and the association between ambient air pollution and early term births (ETB, 37 to 38 weeks) has rarely been studied relative to preterm births (PTB, 28–37 weeks). A time-series study was conducted in Shiyan, a medium-sized city in China. Birth information was collected from the Shiyan Maternity and Child Health Hospital, and 13,111 pregnant women who gave birth between 2015 and 2017 were included. Data of the concentrations of air pollutants, including PM10, PM2.5, NO2, and SO2 and meteorological data, were collected in the corresponding gestational period. The Cox regression analysis was performed to estimate the relationship between ambient air pollution exposure and the risk of preterm birth after controlling the confounders, including maternal age, education, Gravidity, parity, fetal gender, and delivery mode. Very preterm birth (VPTB, 28–32 weeks) as a subtype of PTB was also incorporated in this study. The risk of VPTB and ETB was positively associated with maternal ambient air pollution exposure, and the correlation of gaseous pollutants was stronger than particulate matter. With respect to exposure windows, the critical trimester of air pollutants for different adverse pregnancy outcomes was different. The exposure windows of PM10, PM2.5, and SO2 for ETB were found in the third trimester, with HRs (hazard ratios) of 1.06 (95%CI: 1.04, 1.09), 1.07 (95%CI: 1.04, 1.11), and 1.28 (95%CI: 1.20, 1.35), respectively. However, for NO2, the second and third trimesters exhibited similar results, the HRs reaching 1.10 (95%CI: 1.03, 6.17) and 1.09 (95%CI: 1.03,1.15), respectively. This study extends and strengthen the evidence for a significant correlation between the ambient air pollution exposure during pregnancy and the risk of not only PTB but, also, ETB. Moreover, our findings suggest that the exposure windows during pregnancy vary with different air pollutants and pregnancy outcomes.

A Novel Approach to Estimating Time-Averaged Volcanic SO2 Fluxes from Infrared Satellite Measurements

Long-term continuous time series of SO2 emissions are considered critical elements of both volcano monitoring and basic research into processes within magmatic systems. One highly successful framework for computing these fluxes involves reconstructing a representative time-averaged SO2 plume from which to estimate the SO2 source flux. Previous methods within this framework have used ancillary wind datasets from reanalysis or numerical weather prediction (NWP) to construct the mean plume and then again as a constrained parameter in the fitting. Additionally, traditional SO2 datasets from ultraviolet (UV) sensors lack altitude information, which must be assumed, to correctly calibrate the SO2 data and to capture the appropriate NWP wind level which can be a significant source of error. We have made novel modifications to this framework which do not rely on prior knowledge of the winds and therefore do not inherit errors associated with NWP winds. To perform the plume rotation, we modify a rudimentary computer vision algorithm designed for object detection in medical imaging to detect plume-like objects in gridded SO2 data. We then fit a solution to the general time-averaged dispersion of SO2 from a point source. We demonstrate these techniques using SO2 data generated by a newly developed probabilistic layer height and column loading algorithm designed for the Cross-track Infrared Sounder (CrIS), a hyperspectral infrared sensor aboard the Joint Polar Satellite System’s Suomi-NPP and NOAA-20 satellites. This SO2 data source is best suited to flux estimates at high-latitude volcanoes and at low-latitude, but high-altitude volcanoes. Of particular importance, IR SO2 data can fill an important data gap in the UV-based record: estimating SO2 emissions from high-latitude volcanoes through the polar winters when there is insufficient solar backscatter for UV sensors to be used.

Assessing the Relationship between Economic Growth and Emissions Levels in South Africa between 1994 and 2019

The objective of this study is to establish whether there is any relationship between economic growth and emission levels for pollutants (namely carbon dioxide (CO2), black carbon (BC), sulfur dioxide (SO2), and carbon monoxide (CO)) in South Africa, for the period from 1994 to 2019. Data from the world bank, namely gross domestic product (GDP) and CO2 emissions, were used. BC, SO2, and CO data were obtained from Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2). The linear correlation coefficient and the environmental Kuznets curve (EKC) hypothesis test were used to determine the relationships. The sequential Mann–Kendall (SQMK) test was further used to study the trends. A correlation coefficient of 0.84, which indicates a strong positive linear correlation, between GDP and CO2 emission was observed. However, the relationship between GDP and CO concentration showed a correlation coefficient of −0.05, indicating no linear relationship between the two variables. The EKC hypothesis showed an N-shape for SO2 and CO. Overall, the results of this study indicate that emissions levels are generally correlated with economic growth. Therefore, a stringent regulatory system is needed to curtail the high emissions levels observed in this study, given the devastating impacts of global warming already ravaging the world.