Monthly Average Concentration Research Articles

Long-term exposure to ambient particulate matter is associated with prognosis in people living with HIV/AIDS: Evidence from a longitudinal study

BackgroundEvidence on the association between particulate matter (PM) exposure and prognosis in people living with HIV/AIDS (PWHA) is scarce. We aim to investigate the associations of long-term exposure to PM with AIDS-related deaths and complications. MethodsWe collected follow-up information on 7444 PWHAs from 2000 to 2021 from the HIV/AIDS Comprehensive Response Information Management System of the Wuhan Center for Disease Control and Prevention. The AIDS-related deaths and complications were assessed by physicians every 3 to 6 months, and the monthly average PM concentrations for each PWHA were extracted from the China High Air Pollutants dataset. We employed time-varying Cox regression models to evaluate the associations of the average cumulative PM exposure concentrations with AIDS-related deaths and complications, as well as the mediating effects of AIDS-related complications in PM-induced AIDS-related deaths. ResultsFor each 1 μg/m3 increase in PM1, PM2.5, and PM10, the adjusted hazard ratios (HRs) for AIDS-related deaths were 1.021 (1.009, 1.033), 1.012 (1.005, 1.020), and 1.010 (1.005, 1.015), respectively; and the HRs for AIDS-related complications were 1.049 (1.034, 1.064), 1.029 (1.020, 1.038), and 1.031 (1.024, 1.037), respectively. AIDS-related complications mediated 18.38 % and 18.68 % of the association of exposure to PM1 and PM2.5 with AIDS-related deaths, respectively. The association of PM exposure with AIDS-related deaths was more significant in older PWHA. Meanwhile, the association between PM exposure and AIDS-related complications was stronger in PWHA with a BMI ≥ 24 kg/m2. ConclusionLong-term exposure to PM is positively associated with AIDS-related deaths and complications, and AIDS-related complications have mediating effects in PM-induced AIDS-related deaths. Our evidence emphasizes that enhanced protection against PM exposure for PWHAs is an additional mitigation strategy to reduce AIDS-related deaths and complications.

Remote Sensing Model for Estimating Atmospheric PM2.5 Concentration in the Guangdong-Hong Kong-Macao Greater Bay Area

PM2.5 is extremely harmful to the atmospheric environment and human health, and a timely and accurate understanding of PM2.5 with high spatial and temporal resolution plays an important role in the prevention and control of air pollution. Based on multi-angle implementation of atmospheric correction algorithm (MAIAC), 1 km AOD products, ERA5 meteorological data, and pollutant concentrations (CO, O3, NO2, SO2, PM10, and PM2.5) in the Guangdong-Hong Kong-Macao Greater Bay Area during 2015-2020, a geographically and temporally weighted regression model (GTWR), BP neural network model (BPNN), support vector machine regression model (SVR), and random forest model (RF) were established, respectively, to estimate PM2.5 concentration. The results showed that the estimation ability of the RF model was better than that of the BPNN, SVR, and GTWR models. The correlation coefficients of the BPNN, SVR, GTWR, and RF models were 0.922, 0.920, 0.934, and 0.981, respectively. The RMSE values were 7.192, 7.101, 6.385, and 3.670 μg·m-3. The MAE values were 5.482, 5.450, 4.849, and 2.323 μg·m-3, respectively. The RF model had the best effect during winter, followed by that during summer, and again during spring and autumn, with correlation coefficients above 0.976 in the prediction of different seasons. The RF model could be used to predict the PM2.5 concentration in the Greater Bay Area. In terms of time, the daily ρ(PM2.5) of cities in the Greater Bay Area showed a trend of "decreasing first and then increasing" in 2021, with the highest values ranging from 65.550 μg·m-3 to 112.780 μg·m-3 and the lowest values ranging from 5.000 μg·m-3 to 7.899 μg·m-3. The monthly average concentration showed a U-shaped distribution, and the concentration began to decrease in January and gradually increased after reaching a trough in June. Seasonally, it was characterized by the highest concentration during winter, the lowest during summer, and the transition during spring and autumn. The annual average ρ(PM2.5) of the Greater Bay Area was 28.868 μg·m-3, which was lower than the secondary concentration limit. Spatially, there was a "northwest to southeast" decreasing distribution of PM2.5 in 2021, and the high-pollution areas clustered in the central part of the Greater Bay Area, represented by Foshan. Low concentration areas were mainly distributed in the eastern part of Huizhou, Hong Kong, Macao, Zhuhai, and other coastal areas. The spatial distribution of PM2.5 in different seasons also showed heterogeneity and regionality. The RF model estimated the PM2.5 concentration with high accuracy, which provides a scientific basis for the health risk assessment associated with PM2.5 pollution in the Greater Bay Area.

Internal loading of phosphorus in streams described by a Sediment-Water Exchange Model for Phosphorus (SWEMP): From lab to field scale

The reaction of phosphorus (P) between sediments and water in streams strongly affects the surface water P concentrations. A new reactive transport model (SWEMP: Sediment-Water Exchange Model for Phosphorus) was developed to describe redox dependent P sorption in the sediment and vertical diffusive transport of solutes to the overlying stream. The model parameters were independently obtained to first predict P release in ten different sediment-water batch systems and in two flumes. Input parameters are the degree of P saturation of the sediment, its organic matter content, dissolved oxygen (DO) concentration and temperature. The dissolved P concentrations in the overlying waters ranged from 0.02 to 1.2 mg P L−1 in these systems and were correctly predicted by the model within, on average, a factor 1.3 (batch) or 1.1 (flume). The P flux from the sediment towards the overlying water increased with increasing sediment P:Fe ratio and respiration rates, and with decreasing DO and water pH. After validation of the model with experimental data, it was used to predict monthly P concentrations in Flemish rivers using the total P emission data, total discharge, average sediment properties and the monthly averaged water temperatures, DO concentrations and electric conductivity. The monthly average P concentrations oscillate annually between 0.24 and 0.73 mg P L−1 and predictions matched the long-term monitoring data within 10 % using only one adjustable parameter for the entire water system (N > 250,000). The model predicts that summer peaks in P are related to internal loading from the sediment under anoxic conditions rather than to emission-dilution effects, i.e. external input of P and/or its concentration at lower flow rates. This suggests that, surface water P concentrations can be lowered by enhanced DO in the water, the addition of Fe and Al rich binding agents to the sediments and by reducing P emissions.

Spatial and Temporal Variations in PM1 Concentration and its Correlation with AOD in China from 2014 to 2017

Based on the PM1 mass concentration data from all the air quality monitoring stations in China from 2014 to 2017, the temporal and spatial distribution characteristics of PM1 concentration were studied using the time series statistical and spatial hierarchical clustering methods, and the PM1 spatiotemporal evolution characteristics were revealed. Combined with AOD data of the MODIS remote-sensing satellite, the temporal and spatial variation in PM1-AOD correlation was analyzed on a fine scale. The results showed that, from 2014 to 2017, the annual average PM1 concentration in China decreased yearly, the seasonal PM1 concentration showed the characteristics of "high in winter and low in summer," and the monthly average PM1 concentration showed a "U"-shaped variation. An "M"-shaped PM1 variation pattern was presented before and after the holidays. Weekly variation showed that high PM1 values occurred on Mondays and Fridays, and low ones occurred on Sundays. Based on the spatial clustering method, the national average annual PM1 concentration in China was divided into seven categories, and the overall spatial distribution pattern was "high in the east and low in the west and high in the north and low in the south." The highest and the lowest values of average PM1 concentration occurred in central China(54.59 μg·m-3) and in Xinjiang-Qinghai-Xizang(11.37 μg·m-3), respectively. The PM1-AOD relationship was positively correlated as a whole, the highest correlation coefficient was 0.55 in central China, and the lowest value was 0.36 in central and southern China.

Middle-term nitrogen dioxide exposure and electrocardiogram abnormalities: A nationwide longitudinal study

BackgroundRecently, professionals, such as those from the World Health Organization, have recommended a rigorous standard for nitrogen dioxide (NO2), a typical urban air pollutant affected by regular traffic emissions, based on its short-term and long-term cardiorespiratory effects. However, the association between middle-term NO2 exposure and cardiovascular disorders remains unknown. ObjectivesThis study was conducted to examine the relationship between NO2 exposure and its middle-term cardiovascular risks indicated by electrocardiogram (ECG) abnormalities. MethodWe included 61,094 subjects (132,249 visits) with repeated ECG observations based on longitudinal data from the China National Stroke Screening Survey (CNSSS). The NO2 exposure concentration was derived from a predictive model, measured as the monthly average concentration in the 6 months of preceding the ECG measurement. We used the generalized estimation equation to assess the association between NO2 exposure and ECG abnormalities. ResultFor each 10 µg/m3 increase in monthly average NO2 concentration, the odds ratio of ECG abnormalities was 1.10 (95% confidence interval [CI] 1.09–1.12) after multiple adjustments. Stratified regression analyses of urban and rural residents showed associations between middle-term NO2 exposure and ECG abnormalities in urban (OR 1.09 [95% CI 1.08–1.11]) and rural residents (OR 1.14 [95% CI 1.10–1.19]). The association was robust within different subpopulations. Associations generally remained statistically significant (OR 1.03 [95% CI 1.02–1.05]) after extra adjustment for PM2.5. Exposure-response relationship analysis revealed a nearly linear relationship between NO2 exposure and the risk for ECG abnormalities. ConclusionUsing the variation in ECG signals as a potentially reversible indicator for subclinical risk in cardiovascular systems, our study provides additional evidence on the increased risk posed by middle-term NO2 exposure. Our study showed that policies controlling for NO2 concentrations are beneficial to prevent cardiovascular diseases among Chinese adults.

A dataset of global CO2 concentration in global middle and low latitudes based on GOSAT satellite data during 2009–2020

In response to the increasingly intensifying greenhouse effect, Countries around the world jointly signed the Paris Agreement, and China also made plans and policies of peak carbon dioxide emissions, carbon neutral plans. Carbon dioxide is the focus of international concern as the most important greenhouse gas. So, it’s crucial to know how to obtain carbon dioxide concentration of high precision, high resolution temporal and spatial distribution for advancing the top-down assessment of carbon source, carbon sink and carbon neutral research. This paper creatively proposes a method to obtain high-precision and high-resolution temporal and spatial distribution map of global carbon dioxide concentration by using satellite data. We constructed a new prior time curve parameter library for fitting time domain information. In this paper, we used the transfer learning theory to integrate the time information as a prior profile into the spatial information based on the global data of GOSAT satellite. The spatial prediction information was adjusted to obtain more accurate spatio-temporal prediction of carbon dioxide concentration. The spatio-temporal resolution of the product database is 0.25°. Finally, the database has been compared with TCCON data in middle and low latitudes, which shows the correlation coefficient R and RMSE is 0.98 and 1.38 ppm of the monthly average carbon dioxide concentration respectively. The recommended database can be applied to the calculation of carbon sources and carbon sinks on a large scale.

Identifying the influence of natural and human factors on seasonal water quality in China: current situation of China's water environment and policy impact.

Agricultural production, urbanization, and other anthropogenic activities, the major causes of surface water pollution in China, have dramatically altered hydrological processes and nutrient cycles. Identifying and quantifying the key factors affecting water quality are essential for the better prevention and management of water pollution. However, due to the limitations of traditional statistical analysis methods, it is difficult to evaluate the spatial changes and interactions of influencing factors on water quality. In addition, research on a national scale is difficult, as it involves large-scale and long-term water quality monitoring work. In this study, we collected and collated the monthly average concentrations of four water quality parameters, dissolved oxygen, ammonia nitrogen, chemical oxygen demand, and total phosphorous, based on data from 1547 water quality monitoring stations in China. The combined pollution level of the water quality was assessed using the water quality index. Based on the water quality characteristics, water quality monitoring sites in the dry and wet seasons were grouped using k-means clustering. Eleven environmental factors were evaluated using geodetector software, including six human factors and five natural factors. The results showed that there are high-risk areas for water quality pollution in the eastern and southeastern coastal regions of China in both the dry and wet seasons and that surface water pollution in China is highly spatial heterogenous in both the dry and wet seasons. Among the anthropogenic factors, urban land area is the main factor of water quality pollution in the dry season, and the explanation rate of spatial heterogeneity of integrated water quality pollution index is 20.3%. The number of poultry farms and the area of farmland explained 12.4% and 12.1% of the integrated water quality pollution index in the wet season. The nonlinear relationship between these three anthropogenic and natural factors and their interaction exacerbated water quality pollution. Based on this analysis, we identified the key factors affecting surface water quality in China during the dry and wet seasons, evaluated the achievements of the water environmental protection policies in China in recent years, and proposed future management measures for the effective prevention and control of water quality pollution in high-risk areas.

Spatiotemporal distribution, source apportionment and health risk assessment of atmospheric volatile organic compounds using passive air samplers in a typical coastal area, China

To fully understand source characteristics and human health effects of atmospheric volatile organic compounds (VOCs), it is of great importance to implement long-term measurements, precise source apportionment and simulation. In this study, a 12-month measurement of VOCs was conducted in a coastal area in East China by a novel passive air sampler (PAS). A total of 28 VOC species, mainly in groups of aromatics and halohydrocarbons were observed. The monthly average concentrations of TVOC were as follows: industrial sites (154.10–727.19 μg m−3) > business sites (65.83–86.96 μg m−3) > residential sites (59.63–60.05 μg m−3) > background site (57.62 μg m−3). Their seasonal variation characteristics were: winter > spring > autumn > summer, which was more apparent at industrial sites due to human activities. Six VOC sources including industrial processes, organic solvents, dyeing, plastic synthesis, traffic emissions, and pesticides identified by the positive matrix factorization model compared favorably with the local emissions at industrial sites. Bromoform, hexachloro-1,3-butadiene, C1–C3 halogenated alkanes and benzene homologs could be the important indicators of industrial VOC emissions. The halohydrocarbons and aromatics were the major contributors to the ozone formation potential (OFP) and secondary organic aerosol potential (SOAP) in industrial areas, respectively. Both of OFP and SOAP have their maximum values in winter due to high VOC concentrations. Health risk assessment indicated that a high probability of non-carcinogenic risk (HI > 1) was in the region at 10 m height and 50m radius, and minor carcinogenic risk (CR > 10−6) was in the region at 70 m height and 1000 m radius. Among all the compounds, 1,2-Dibromo-3-chloropropane was the largest contributor with the individual HI value (17.99) and carcinogenic risk (5.34 × 10−3) at the peak. In this study, the PAS as a sampling tool was proven to be reliable and efficient for long-term measurements in coastal areas. It is worth to have further studies to promote passive sampling as an alternative to VOCs monitoring methods.

SPATIO-TEMPORAL VARIABILITY OF AIR QUALITY AND RELATIONSHIP WITH METEOROLOGICAL PARAMETERS IN CAPE TOWN, SOUTH AFRICA

Abstract. African cities are growing fast, and this rapid urbanisation has tremendously increased air pollution and greenhouse gas emissions. Despite this disturbing reality, the deleterious impacts of air pollution on livelihoods and the environment are often overlooked. Recently, the link between air quality and meteorological parameters has received attention from researchers and understanding this relationship could significantly improve our understanding of the spatial and temporal dynamics of air quality. This study focuses on analysing the spatiotemporal variation of three key air quality parameters, namely nitrogen dioxide (NO2), sulphur dioxide (SO2), and particulate matter (PM10), in Cape Town between 2020 and 2021. The study also aims to assess the relationship between air quality and meteorological parameters during this period, and the compliance with national and international air quality guidelines. Air quality data were collected from five monitoring stations in the City of Cape Town. A preliminary analysis of the data reveals high increases in the concentration of air pollutants from 2020 to 2021. For instance, the average monthly concentration of NO2 and SO2 at Bellville South station more than doubled during this period (from 6.7–14.8 µg/m3 and 3.4–8.1 µg/m3, respectively). This is worrisome as the air quality index (AQI) exceeded the safe limits at several sites. There is a need for urgent action by national and city governments in Africa to invest in air quality monitoring systems to enhance the well-being of citizens and promote the long-term sustainability of cities and infrastructure.

Temporal evolution of PM2.5, PM10, and total suspended particles (TSP) in the Ciuc basin (Transylvania) with specific microclimate condition from 2010 to 2019

Modern societies are characterized by increased air pollution, and particulate matter (PM) is one of the most significant air pollutants and is a major environmental health problem. Therefore, long- and short-term exposure via inhalation, ingestion, and dermal absorption of particulate matter may cause series health issues, such as cardio pulmonary and lung cancer disease. Air pollutants accumulation is significantly higher in closed regions or basins characterized by strong thermal inversions, especially during the cold period such in case of the Ciuc basin. The aim of this study was to carry out the time series analysis of PM2.5, PM10 and TSP in the Ciuc basin for the period 2010–2019, in order to decipher the main characteristics of air pollution in this region.The data obtained were analyzed on a daily, monthly and annual basis by different statistical methods. The average monthly concentration of TSP (60.03 µg m−3), PM10 (19.21 µg m−3) and PM2.5 (14.73 µg m−3) particulate matter in the studied regions varied between 29.84–134.79 µg m−3, 4.38–63.51 µg m−3 and 4.01–54.41 µg m−3, respectively. Regarding the ratio of PM2.5 and PM10 in the total particulate matter (TPM) was 0.25 and 0.33. Due to meteorological factors and emission fluctuations, particulate matter exhibits high seasonal variations, therefore the highest concentrations were recorded during the cold period, while the lowest values were observed in summer. The percentage of PM10 exceedances (50 µg m−3) represents 24.8% in winter, meanwhile in autumn and spring a significantly lower exceedances percentage was observed, 2.6% and 1.7%, respectively. The correlation analysis revealed that the correlation level of the studied pollutants varied between 0.73–0.78.

COMPARISON OF COMPREHENSIVE ENVIRONMENTAL MONITORING RESULTS OF THE CITY OF KAMIANSKE USING DIFFERENT MEASUREMENT METHODS

Comprehensive assessment of the main environmental parameters in Ukraine in accordance with EU Directives requires a thorough improvement of the environmental monitoring system. In the present time, the assessment of atmospheric air quality is carried out by an automated and instrumental method of determining the concentrations of pollutants, each of which has advantages and disadvantages. Therefore, the purpose of the work was to conduct a comparative analysis of the reliability of the results of measuring the concentrations of pollutants in atmospheric air. The study was conducted on the example of average monthly dust concentrations measured over 18 months by an automated and instrumental method at one of the stationary observation posts in the city of Kamianske. According to the results of observations, it was found that according to instrumental methods, the average monthly concentrations of dust are 10—50 times higher than the indicators obtained by automated measurement methods, however, none of the methods revealed an excess of the maximum permissible concentrations in the atmospheric air of coarse dust. In order to verify the effectiveness of the city's environmental monitoring system, a comparison of the average monthly volume of dust emissions with its average monthly concentrations measured by automated and instrumental methods was carried out. The developed mathematical models of a polynomial nature indicate the dependence of average monthly concentrations on the average monthly volume of emissions, which is confirmed by high correlation coefficients r = 0.658 and r = 0.975 for automated and instrumental measurement methods, respectively. The established reliability of the results of measurements of average monthly concentrations of dust in the atmospheric air requires thorough research due to increasing the number and frequency of obtaining monitoring data, considering meteorological parameters, relief-forming factors, etc. It was noted that the primary task of improving the city's environmental monitoring system is to equip existing observation posts with devices for monitoring dust PM2.5 and PM10, as well as to increase the number of active environmental monitoring posts.

Characterizing spatiotemporal patterns of elevated PM2.5 exposures in a megacity of China using combined mobile and stationary measurements

Exposure to PM2.5 (particles with an aerodynamic diameter equal to or less than 2.5 μm) is associated with a variety of negative health outcomes. Measurements from sparsely situated air quality monitoring stations (AQMSs) may be inappropriate to represent real PM2.5 exposures, particularly in traffic-related environments. In this study, efforts were made to characterize spatiotemporal variation of PM2.5 pollution over Shenzhen, China from July 2019 to June 2020 using combined mobile (on-road PM2.5) and stationary (AQMS PM2.5) measurements. Monthly-average concentrations of on-road PM2.5 ranged from 10.4 ± 6.1 to 47.3 ± 23.9 μg/m3, and showed consistent trend with AQMS PM2.5 concentrations which ranged from 8.3 ± 3.1 to 37.2 ± 12.9 μg/m3. On-road PM2.5 and AQMS PM2.5 concentrations dropped by 54.6% and 30.2% in February 2020, probably due to the low anthropogenic emissions during the period of Spring Festival and COVID-19 lockdown. Weekend effect on both on-road and AQMS PM2.5 concentrations was not noticeable. Relative high on-road PM2.5 concentrations were observed during morning and evening rush hours. An “elevated concentration” concept was applied to estimate the influence of emissions on PM2.5 exposure. Elevated concentration showed strong diurnal and spatial variation, and was about 5.0 μg/m3 on-average. Mappings of on-road PM2.5 and elevated concentrations confirmed the heterogeneity of spatial distribution of PM2.5 exposures in Shenzhen where PM2.5 pollution was more severe in western and northern areas. Our results highlight the elevated PM2.5 exposures in traffic-related environments, and the inequity in urban exposure levels and health.

Filtration columns containing waste iron shavings, loofah, and plastic shavings for further removal of nitrate and phosphate from wastewater effluent

A novel pilot-scale advanced treatment system combining waste products as fillers is proposed and established to enhance the removal of nitrate (NO3−-N) and phosphate (PO43−-P) from secondary treated effluent. The system consists of four modular filter columns, one containing iron shavings (R1), two containing loofahs (R2 and R3), and one containing plastic shavings (R4). The monthly average concentration of total nitrogen (TN) and total phosphorus (TP) decreased from 8.87 to 2.52 mg/L and 0.607 to 0.299 mg/L, respectively. Micro-electrolysis of iron shavings produces Fe2+ and Fe3+ to remove PO43−-P, while oxygen (O2) consumption creates anoxic conditions for subsequent denitrification. Gallionellaceae, iron-autotrophic Microorganisms, enriched the surface of iron shavings. The loofah served as a carbon source to remove NO3−-N, and its porous mesh structure facilitated the attachment of biofilm. The plastic shavings intercepted suspended solids and degraded excess carbon sources. This system can be scaled up and installed at wastewater plants to improve the water quality of effluent cost-effectively.

A Comparative Study of Air Pollutant Concentrations before the COVID-19 Pandemic and in the New Normal in the Región de Murcia (Spain)

The management of the COVID-19 pandemic, which required global lockdowns and mobility restrictions, positively impacted air quality (especially the concentrations of primary pollutants). With the progressive return to normal activity during the last few months of 2020 and 2021, air pollutant concentrations were expected to rise again. In this work, together with meteorological conditions and traffic intensity, the concentrations of NO, NO2, O3, PM10, and SO2 measured at three different locations (urban, industrial, and rural) in southeast Spain, from January 2016 to December 2021, were analyzed. For the three locations, PM10 concentrations did not change significantly in 2020 or 2021. SO2 concentrations did not experience changes at the industrial site in 2020 or 2021, but SO2 concentrations did slightly increase in five months of 2021 in the urban location (with a maximum monthly average concentration of 11 μg·m−3). In 2020, NO and NO2 concentrations were, in general, lower than those measured in previous years (2016–2019). Interestingly, at the urban site (San Basilio), the levels of NO and NO2 were significantly lower in 2021 than in 2020 despite the higher traffic intensity in 2021. While the concentrations of NO and NO2 decreased, in VOC-limited areas, O3 concentrations increased. This was clearly observed at the industrial and the urban sites. Despite the increase, O3 concentrations were still below the European O3 target value for the protection of human health. An analysis of new registrations of EVs and HEVs in the latter years and their impact on the vehicle fleet of Murcia and, subsequently, on road emissions was carried out. Regardless of the steep increase in environmentally friendly vehicles in 2021, their share of the vehicle fleet is still negligible; thus, the lower concentrations of NO and NO2 are likely due to more favorable weather conditions in 2021 than in 2020.

Distribution of Bacterial Concentration and Viability in Atmospheric Bioaerosols Under Different Weather Conditions in the Coastal Region of Qingdao

Bacteria are the most abundant microorganisms in atmospheric bioaerosols, widely distributed in the environment. Bioaerosol samples were collected using the FA-1 impact sampler from October 2013 to January 2021 in the coastal city of Qingdao, and samples stained with a BacLightTM bacterial viability kit were used to measure the concentrations of viable and non-viable bacteria with an epifluorescence microscope. The viable and non-viable bacteria in bioaerosols were characterized during different seasons, with particular attention paid to the distribution characteristics of bacteria on foggy, hazy, and dust days. The results showed that the mean concentrations of total bacteria were (1.06±0.68)×105 cells·m-3 in Qingdao during the sampling period, and those of viable and non-viable bacteria were (8.20±4.88)×103 cells·m-3and (9.74±6.72)×104 cells·m-3, respectively. The seasonal concentrations of non-viable bacteria were the highest in spring and winter and the lowest in summer, whereas that of viable bacteria was highest in spring, followed by those in summer and autumn, and the lowest in winter. Atmospheric bacterial concentrations fluctuated with by month, and total bacteria presented a similar variation pattern with that of non-viable bacteria. The monthly average concentration of non-viable bacteria showed the highest value in March during the spring and the lowest in June during the summer in 2021, whereas the highest value for viable bacteria occurred in May during the spring in 2021 and the lowest in December during the winter in 2020. Viable bacteria concentrations were significantly positively correlated with temperature and significantly negatively correlated with NO2, SO2, and CO. Non-viable bacteria were significantly positively correlated with PM. The bacteria in bioaerosol particles showed bimodal, unimodal, and skewed size distributions, varying with season and month. Under different weather conditions, the concentration of non-viable bacteria on dusty days was significantly higher than that on sunny, foggy, or hazy days, but the bacteria viability was as low as 6.85% due to long-distance transport. Anthropogenic pollution resulted in the lowest viability of bacteria at 4.10% on hazy days, whereas the highest viability in bacteria was 16.26% on foggy days due to high humidity. The size distribution of bacteria in bioaerosol particles under different pollution days showed a bimodal distribution, and the peak size depended on the weather type.

SPATIAL AND TEMPORAL QUANTIFICATION OF PARTICULATE MATTER (PM10) AND ASSOCIATED HEAVY METALS IN URBAN ENVIRONMENT OF DELHI

Atmospheric concentrations of PM10 and associated heavy metals were quantified to study the air pollution load of two distinct location of Delhi viz. (1) Agricultural farms of Indian Agricultural Research Institute (IARI) and (2) Yamuna flood plains (YFP). The air sampling has been done for two consecutive years in winter season, comprising the months from November to April in 2020- 21 and 2021-22 respectively. The glass fiber filter paper was used for the particulate matter deposition in high volume sampler. The monthly average concentration of PM10 varies from 112µg/ m3 to 187µg/m3 at IARI and 205 µg/m3 to 364 µg/m3 at YFP. The highest concentration of PM10 was recorded in the month of January. The concentration of PM10 and associated heavy metals were significantly higher at the YFP area. The heavy metal concentration load was 18% and 27% more in the YFP in comparison to the IARI farms in the year 2020-21 and 2021-22 respectively. The higher concentration of heavy metals recorded at YFP because of its location which is cross section of roads having very heavy traffic throughout 24 hrs. and a thermal power plant nearby. The heavy metals concentration trend at each location was in the order of Zn > Fe>Pb>Ni>Cd. The concentration of Pb, Ni, and Cd was significantly less at IARI in comparison to YFP.

Public monitoring as a tool for assessing the state of the air basin in the Ukrainian Regions

Over the recent years automated observation systems that work continuously and allow obtaining information at any time have been widely implemented. Such systems include public monitoring systems in order to receive more complete information about the quality characteristics of the air basin over Ukraine. Examples of public monitoring organizations include SaveEcoBot, EcoCity, SaveDnipro.
 The information presented on the websites of the public monitoring system contains large amount of data that are difficult to process and analyze. In order to optimize data processing, a program called PublicMonitoringParser was developed. It transforms large volumes of data presented on the Internet resource SaveEcoBot.com into compact, convenient, small files containing average monthly concentrations of pollutants and indicators of meteorological parameters. The program is written using the C# programming language and the .NET platform. The structure of the program and the principles of its operation are presented in this research.
 According to the data of the website SavEecoBot.com the developed program was applied for assessment of the air basin state of Lviv Region in 2021. In general, for most substances, monitoring data were available only at the station in the city of Lviv at the following address: 13 Plastova Str. Monitoring of PM2.5 and PM10 content was carried out across the whole region.
 It was detected that the content of NO2, SO2, NH3 exceeded the limit value. Significant excesses were also noted for the NH3 content (up to 9 – 16 MPC). It was established that the location of the public monitoring station was surrounded by industrial buildings, numerous transport industry enterprises, an oil depot for OKKO gas stations and city sewage treatment facilities. A comparison of the data obtained at stationary and automated points that monitor individual pollutants showed that the results are comparable only for the NO2 content.
 Data from automated observation points complement the network of stationary points. Currently, the existing stationary network of atmospheric pollution monitoring across most regions of Ukraine is outdated and needs to be re-equipped. In general, it is necessary to further implement automated systems that are able to characterize the state of the air basin across individual populated places in a more complete and detailed manner.

Early Warning and Joint Regulation of Water Quantity and Quality in the Daqing River Basin

In the recent decades, the issue of water-resource security of the Daqing River Basin, which is one of the five major rivers in the Haihe River Basin, has become increasingly serious affected by climate change and human activities. In this paper, a dynamic simulation and early warning model of water quantity and quality in this basin based on the SWAT model was constructed to promote the implementation of water environment quality and safety bottom line in the Beijing-Tianjin-Hebei region. The results of the study are as follows: (1) When encountering a once-in-a-century rainstorm, the flood pressure of Zijingguan in the flood season is the highest, with the highest water level reaching 521.23 m, and the overall maximum runoff follows the order of Zijingguan > Fuping > Zhangfang. (2) When the NH3-N emissions are reduced by 37.64~85.10% in each month (based on the level in 2017), the water quality at the outlet of the basin can reach the standard, and the upper limit of NH3-N emissions is 504.5 t/m. (3) The regulation and control scheme seeking to “ensure the base flow with standard water quality” and “optimize NH3-N annual emission” is proposed in this paper. The NH3-N concentration at the outlet of all watersheds can reach the standard when the basic runoff of each sub-basin reaches 0.01 to 10.32 m3/s. In addition, concentrating the emission in July, August, and September and reducing the emission intensity of NH3-N in proportion can significantly reduce the monthly average NH3-N concentration (<1.99 mg/L) at the outlet section of the basin.

Estimation of chromophoric dissolved organic matter and its controlling factors in Beaufort Sea using mixture density network and Sentinel-3 data

With the warming of the high-latitude regional climate, melting of permafrost, and acceleration of hydrological cycles, the Arctic Ocean (AO) has undergone a series of rapid changes in the past decades. As a dominant optical component of the AO, the variations in chromophoric dissolved organic matter (CDOM) concentration affect the physiological state marine organisms. In this study, machine learning retrieval model based on in situ data and mixture density network (MDN) was developed. Compared to other models, MDN model performed better on test data (R2 = 0.83, and root mean squared error = 0.22 m−1) and was applied to Sentinel-3 OLCI data. Afterward, the spatiotemporal distribution of CDOM during the ice-free (June–September) from 2016 to 2020 in the Beaufort Sea was obtained. CDOM concentration generally exhibited an upward trend. The maximum monthly average CDOM concentration appeared in June and gradually decreased thereafter, reaching its lowest value in September of each year. The maximum value appeared in June 2020 (0.91 m−1), and the minimum value was observed in September 2017 (0.81 m−1). The CDOM concentration nearshore was higher than that in other areas; and gradually decreased from offshore to the open sea. CDOM was highly correlated with salinity (R2 = 0.49) and discharge (R2 = 0.53), and the tight correlation between salinity and CDOM further suggested that terrestrial inputs were the main source of CDOM in the Beaufort Sea. However, sea level pressure contributed to the spatial variations in CDOM. When southerly wind prevailed and wind direction was aligned with the CDOM diffusion direction, the wind accelerated the diffusion of CDOM into the open sea. Meanwhile, seawater was diluted by the sea ice melting, resulting in decrease in CDOM concentration. Herein, this paper proposed a robust and near real-time method for CDOM monitoring and influence factor analysis, which would promote the understanding of AO CDOM budgets.

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.