Automatic Monitoring Stations Research Articles

Monitoring Dissolved Organic Carbon Concentration and Flux in the Qiantang Riverine System Using Sentinel-2 Satellite Images

Real-time monitoring of riverine-dissolved organic carbon (DOC) and its controlling factors is critical for formulating strategies regarding the river basin and marginal seas pollution prevention and control. In this study, we established a linear regression formulation that relates the permanganate index (CODMn) to the DOC concentration based on in situ measurements collected on five field surveys in 2023–2024. This regression formulation was used on a large number of data collected from automatic monitoring stations in the Qiantang River area to construct a daily quasi-in situ database of DOC concentration. By combining the quasi-in situ DOC data and Sentinel-2 measurements, an enhanced algorithm for empirical DOC estimation was developed (R2 = 0.66) using the extreme gradient boosting (XGBoost) method and its spatial and temporal variations in the Qiantang River were analyzed from 2016 to 2023. Spatially, the main stream of the Qiantang River exhibited an overall decreasing and increasing trend influenced by population density, economic development, and pollutant discharge in the basin area, and the temporal distribution of DOC was controlled by meteorological conditions. The DOC contents had the highest in summer, primarily due to high rainfall and leaching. The inter-annual variation in DOC concentration was influenced by the total annual runoff volumes, with a minimum level of 2.24 mg L−1 in 2023 and a maximum level of 2.45 mg L−1 in 2019. The monthly DOC fluxes ranged from 6.3 to 13.8 × 104 t, with the highest values coinciding with the maximum river discharge volumes in June and July. The DOC levels in the Qiantang River remained relatively high in recent years (2016–2023). This study enables the concerned stakeholders and researchers to better understand carbon transportation and its dynamics in the Qiantang River and its coastal areas.

Complex assessment of ambient particulate matter in a small settlement with coal-handling port

Relevance. Russian coal exports have been steadily increasing in recent years, and Far Eastern ports, originally not adapted to dusty cargoes, have switched to coal operations. Unoptimized coal handling leads to increased content of coal-containing particles in the air and to various environmental problems. Aim. To analyze the concentration of particulate matter in the air and the chemical composition of particles on the surface of conifer needles in the Posyet settlement (Primorsky Krai, Russia), where a large coal terminal operates. Materials and methods. The chemical composition was studied on the surface of conifer needles by X-ray fluorescence and in washout of conifer needle samples using Raman spectroscopy and energy dispersive analysis. Concentrations of particles with the diameter of 2.5 and 10 μm (PM2.5 and PM10, respectively) were measured using automatic monitoring stations. Results and conclusions. Chemical composition was dominated by mineral particles (calcite, silicates, etc.). However, the presence of coal particles (up to 8.3%) and metal particles was also observed. Mean concentrations of PM2.5 and PM10 were within the Russian and international health standards, but close to their upper limits. Concentrations of both measured particle fractions were highest during the winter months (as well as in March, which is traditionally a cold month in the Far East), followed by a gradual decrease. The study obtained new data on the annual variations of concentrations of PM2.5 and PM10, as well as on the basic composition of particles from the surface of woody vegetation (needles) as an indicator of the state of air pollution in this settlement.

Dissolved Oxygen Concentration Prediction in the Pearl River Estuary with Deep Learning for Driving Factors Identification: Temperature, pH, Conductivity, and Ammonia Nitrogen

Predicting the dissolved oxygen concentration and identifying its driving factors are essential for improved prevention and management of anoxia in estuaries. However, complex hydrodynamic conditions and the limitations in traditional methods result in challenges in the identification of the driving factors for the low dissolved oxygen (DO) phenomenon. The objective of our study is to develop a robust deep learning model using four-year in situ data collected from an automatic water quality monitoring station (AWQMS) in an estuary, for accurate identification and quantification of the driving factors influencing DO levels. Mitigations in hypoxia were observed during the initial two years, but a subsequent decline in DO concentrations was witnessed recently. The periodicity of DO concentrations in the Pearl River Estuary reduced with the increase in the hypoxic intensity. Maximal information coefficient (MIC) and extreme gradient boosting (XGBoost) were employed to determine the significance of input variables, which were subsequently validated by using the long- and short-term memory networks (LSTMs). The driving factors contributing to the hypoxia problem were shown as temperature, pH, conductivity, and NH4+-N concentrations. Notably, the evaluation index values of the hybrid model are MAPE = 0.0887 and R2 = 0.9208, which have been improved compared with the LSTM model by about 99.34% in MAPE reduction and 16.56% in R2 improvement, indicating that the MixUp-LSTM model was capable of effectively capturing nonlinear relationships between DO and other water quality indicators. Based on existing literature, three traditional statistical methods and four machine learning models were also performed to compare with the proposed MixUp-LSTM model, which outperformed other models in terms of prediction accuracy and robustness. Overall, the successful identification of the driving factors for the deoxygenation phenomenon would have important implications for the governance and regulation of low DO in estuaries.

Predicting plateau atmospheric ozone concentrations by a machine learning approach: A case study of a typical city on the southwestern plateau of China

Atmospheric ozone (O3) has been placed on the priority control pollutant list in China's 14th Five-Year Plan. Due to their unique meteorological conditions, plateau regions contain high concentrations of atmospheric O3. However, traditional experimental methods for determining O3 concentrations using automatic monitoring stations cannot predict O3 trends. In this study, two machine learning models (a nonlinear auto-regressive model with external inputs (NARX) and a temporal convolution network (TCN)) were developed to predict O3 concentrations in a plateau area in the Kunming region by considering the effects of meteorological parameters, air quality parameters, and volatile organic compounds (VOCs). The plateau O3 prediction accuracy of the machine learning models was found to be much higher than those of numerical models that served as a comparison. The O3 values predicted by the machine learning models closely matched the actual monitoring data. The temporal distribution of plateau O3 displayed a high all-day peak from February to May. A correlation analysis between O3 concentrations and feature parameters demonstrated that humidity is the feature with the highest absolute correlation (−0.72), and was negatively correlated with O3 concentrations during all test periods. VOCs and temperatures were also found to have high positive correlation coefficients with O3 during periods of significant O3 pollution. After negating the effects of meteorological parameters, the predicted O3 concentrations decreased significantly, whereas they increased in the absence of NOx. Although individual VOCs were found to greatly affect the O3 concentration, the total VOC (TVOC) concentration had a relatively small effect. The proposed machine learning model was demonstrated to predict plateau O3 concentrations and distinguish how different features affect O3 variations.

Quantification of Airborne Particulate Matter and Trace Element Deposition on Hedera helix and Senecio cineraria Leaves.

In both developed and developing countries, atmospheric pollution with particulate matter (PM) remains an important issue. Despite the health effects of poor air quality, studies on air pollution are often limited by the high costs of continuous monitoring and the need for extensive sampling. Furthermore, these particles are often enriched with potentially toxic trace elements and organic pollutants. This study evaluates both the composition of atmospheric dust accumulated during a certain timespan on Hedera helix and Senecio cineraria leaves and the potential for their use as bio-monitors. The test plants were positioned near automatic air quality monitoring stations at four different sites with respectively high, moderate and low traffic intensity. The gravimetric deposition of PM10 and PM2.5 on leaves was compared with data recorded by the monitoring stations and related to the weather conditions reported by Argentina's National Meteorological Service. To determine the presence of trace elements enriching the PM deposited on leaves, two analytical techniques were applied: XRF (not destructive) and ICP (destructive). The results indicated that only in the unpaved street location (site 2) did PM10 and PM2.5 concentrations (90 µg m-3 and 9 µg m-3) in the air exceed more than five times WHO guidelines (15 µg m-3 and 5 µg m-3). However, several trace elements were found to be enriching PM deposited on leaves from all sites. Predominantly, increased concentrations of Cd, Cu, Ti, Mn, Zn and Fe were found, which were associated with construction, traffic and unpaved street sources. Furthermore, based on its capability to sequester above 2800 µg cm-2 of PM10, 2450 µg cm-2 of PM2.5 and trace elements, Senecio cineraria can be taken into consideration for adoption as a bio-monitor or even for PM mitigation.

Universal high-frequency monitoring methods of river water quality in China based on machine learning

The in-situ high-frequency monitoring of total nitrogen (TN) and total phosphorus (TP) in rivers is a challenge and key to instant water quality judgment and early warning. Based on the physical and chemical association between TN/TP and sensor-measurable predictors, we proposed a novel “indirect” measurement method for TN and TP in rivers. This method combines the timeliness of multi-sensor and the accuracy of intelligent algorithms, utilizing 188,629 data sets from 131 water monitoring stations across China. Under 5 algorithms and 4 predictor group scenarios, the results showed that: (1) extra tree regression (ETR) with 6 predictors exhibited the best precision, and the mean determination coefficient (R2) of TN and TP inversion across 131 stations reached 0.78 ± 0.25 and 0.79 ± 0.22 respectively; (2) among 6 potential predictors, the importance degrees of temperature, electrical conductivity, NH4-N, and turbidity were greater than that of pH and DO, and >80 % of stations exhibited acceptable prediction accuracy (R2 > 0.6) when the number of predictors (P) ranged from 4 to 6, which showed good tolerability to predictor variations; (3) the accurate classification rates of water quality standard (ACRws) of all stations based on TN and TP reached 90.41 ± 6.96 % and 92.33 ± 6.41 %; (4) in 9 regions/basins of China, this method showed universal application potential with no significant prediction difference. Compared with laboratory test, water quality automatic monitoring station, and remote sensing inversion, the proposed method offers high-frequency, high-precision, regional adaptability, low cost, and stable operation under rainy, cloudy, and nighttime conditions. The new method may provide important technological support for timely pollutant tracing, pre-warning, and emergency control for river pollution.

Assessment of radiological environmental impact under various meteorological condition

The meteorological parameters, e.g., wind direction, wind speed and atmospheric stability, affect greatly the diffusion of pollution and radiological environmental impact assessment. Based on the hourly meteorological data obtained from an automatic monitoring station, the radiological impact indicated by the air concentration, individual dose and maximal individual effective dose were analyzed and compared between 2020 and 2021. The paper concluded that children are the main group to be the most easily infected, and the critical exposure path is internal exposure from inhalation. This paper offers a new window for timely decision-making for radiological safety under different climate conditions.

Research on energy spectrum anomaly detection method for state control radiation environmental monitoring based on LOF algorithm

The state-controlled radiation environment automatic monitoring stations are important facilities for monitoring the quality and trends of the radiation environment. However, the analysis and utilization of monitoring data from these stations have limitations. To address this issue, this paper proposes an approach that utilizes the high-dimensional data visualization method based on t-SNE and PCA-BIRCH algorithm's spectrum clustering model to classify historical spectrum data. Subsequently, a local outlier factor-based environmental spectrum abnormal fluctuation detection model is established to enhance discrimination of non-environmental factors causing spectrum anomalies. Results indicate significant differences in spectrum aggregation under different meteorological conditions. The clustering algorithm effectively separates clear and rainy weather spectra based on spectral features, with minimal time consumption and reduced interference from mixed spectrum data. By utilizing local density features, we establish an anomaly detection model, assigning an abnormal score to each spectrum. Comparative analysis demonstrates improved discriminability of the anomaly detection model for unclassified datasets. Our algorithm accurately identifies spectrum fluctuations caused by non-environmental factors amidst complex backgrounds, offering valuable technical support for environmental quality assurance and nuclear emergency decision-making.

Research on Missing Value Imputation to Improve the Validity of Air Quality Data Evaluation on the Qinghai-Tibetan Plateau

In the Qinghai-Tibet Plateau region, operational deficiencies and limited maintenance capacities often impair automatic air quality monitoring stations. This results in frequent data omissions, compromising the reliability of environmental assessment data. Therefore, an effective data imputation method is required to address the gaps in observational records. Utilizing a Sequence-to-Sequence framework, we introduce a model termed Bidirectional Recurrent Imputation for Time Series-Attention-based Long Short-Term Memory (BRITS-ALSTM). The encoder of BRITS-ALSTM applies BRITS to integrate single-station historical characteristics with multi-station correlation features. Concurrently, the decoder employs LSTM within an attention mechanism to capitalize on previously observed data, thereby generating hourly imputations for missing air quality data values. The model was trained using six types of air quality data from 16 stations across Qinghai Province. Through localized testing and parameter optimization, BRITS-ALSTM achieved a reduction in mean relative error (MRE) by 74.88% compared to the baseline mean-filling approach. Additionally, ablation studies demonstrated an improvement in the coefficient of determination R-squared (R2) from 0.67 to 0.76, outperforming the standalone BRITS. Consequently, BRITS-ALSTM enhances the accuracy of air quality data evaluations in the Tibetan Plateau and offers an efficacious strategy for data imputation in elevated terrains.

Study of PM2.5 and PM10 Concentrations Near the Plating Plant Using Automatic Monitoring Stations

The content of PM2.5 and PM10 in the air at the Dalpribor enterprise in Vladivostok (Russian Federation) was studied. The distribution of galvanic aerosol particles in the area adjacent to the enterprise was studied. The highest concentrations of PM2.5 and PM10 particles were recorded inside the electroplating shop. A significant contribution (exceeding 10 %) of electroplating enterprises to the overall urban air pollution was revealed. It was emphasized that, in general, the measured average concentration of PM2.5 and PM10 particles does not exceed the MAC values.

Spatial distribution prediction model for lake water quality parameters from ultra-sparse sampling data with recent satellite data

ABSTRACT Daily spatial distributions of water quality parameters (WQPs) are a crucial indicator in delicacy management of lake water environment in China. However, because of the high cost of construction and maintenance, there are a few automatic monitoring stations (AMSs) for continuous water sampling with only ultra-sparse spatial sampling data can be obtained. Meanwhile, although WQPs from satellite inversion are spatially continuous, they are missing on most days due to severe affected by weather and satellite revisit cycle, besides the high purchase cost. Thus, the model called MLP-GPR is formulated in this study to address the issue of predicting the spatial distribution of WQPs from ultra-sparse sampling data of AMSs combined with the spatial correlation information based on ultra-sparse satellite inversion data. This model includes the multilayer perceptron model (MLP) and Gaussian process regression model (GPR). MLP is trained to learn the interaction factors of WQPs between different locations through the satellite inversion data for predicting WQPs at more locations based on the data measured by AMSs. Then, GPR regresses the spatial distribution of WQPs at all locations on the entire lake from the predicted data by MLP. Based on the above model, we applied the satellite inversion, AMSs and artificial sampling data to predict the WQP spatial distribution of Shahu Lake in Wuhan, China. The experiment results show that the spatial resolution of WQP distribution predicted by MLP-GPR trained by satellite inversion data can reach 15 m by sampling data from artificial stations on the lake with 3.197 km2. Moreover, for each of these WQPs, compared with the satellite inversion data, the average MRE, R, and NSE of prediction results are below 3.95%, above 0.85, and above 0.69, respectively.

The effect of various air pollution and participants' age on semen quality in southern Taiwan

ObjectiveThis study aimed to investigate the association between semen quality and air pollution in southern Taiwan. Materials and methodsIn this retrospective study, 4338 males aged 21–70 years were recruited between 2001 and 2018 from a reproductive medical center. Semen quality was assessed according to standardized methods outlined in the World Health Organization (WHO) Laboratory Manual 1999 and 2010, including total sperm count, progressive sperm motility (%), rapid progressive sperm motility (%), and sperm with normal morphology (%). All designated national air quality automatic continuous monitoring stations measured the levels of air pollution [particulate matter (PM10 and PM2.5), sulfur dioxide (SO2), nitrogen dioxide (NO2), carbon monoxide (CO), ozone (O3)], and was documented by Environmental Protection Administration in Taiwan. We collected data on the levels of air pollution based on the participants' residential addresses. ResultsIn our study, we found that progressive and rapid progressive sperm motility significantly decreased annually (p < 0.05). In addition, increasing age influenced total sperm count, progressive sperm motility, rapid progressive sperm motility, and sperm with normal morphology (p < 0.05). Among different air pollution, we observed SO2 was associated with lower rapid progressive sperm motility and lower sperm with normal morphology (β = −0.103, p = 0.043; β = 0.118, p = 0.001, respectively). However, NO2 was associated with higher rapid progressive sperm motility and a high number of sperm with normal morphology (β = 0.129, p = 0.002; β = 0.127, p < 0.001, respectively). ConclusionsThe semen quality in southern Taiwan appears to have declined in recent years. The participant's age for semen analysis was most strongly associated with semen parameters, Moreover, a significant association between SO2 and NO2 levels and semen motility was observed, even after adjusting for multiple comparisons. Further study is required to analyze the dose-dependent effect of SO2 and NO2 on semen parameters.

Temporal and Spatial Distributions of O3 Concentration and Potential Source Area Analysis of Hexi Corridor Based on Satellite and Ground Monitoring

Based on 2005-2020 O3 column concentration data of OMI remote sensing satellite, combined with air pollutant data from 10 nationally controlled environmental automatic monitoring stations in the Hexi Corridor and global data assimilation system meteorological data, we used Kriging interpolation, correlation analysis, and backward trajectory (HYSPLIT) models to explore the temporal and spatial distribution characteristics, meteorological factors, transmission paths, and potential sources of O3 in the Hexi Corridor. The results showed the following:① in terms of temporal distribution, O3 column concentration showed an upward trend in 2005-2010 and 2014-2020 and downward trend in 2010-2014; the maximum and minimum values were reached in 2010 and 2014 (332.31 DU and 301.00 DU), respectively, and seasonal changes showed that those in spring and winter were significantly higher than those in summer and autumn. ② In terms of spatial distribution, O3 column concentration showed a latitudinal band distribution characteristic of increasing from southwest to northeast; the high-value areas were primarily distributed in urban areas with low terrain, and the median zone was latitudinally striped with the basic alignment of the Qilian foothills. ③ The analysis of meteorological conditions revealed that temperature, wind speed, and sunshine hours were positively correlated with O3, and relative humidity was negatively correlated with O3. ④ By simulating the airflow transportation trajectory of the receiving point in Wuwei City, it was found that the direction of the O3 conveying path was relatively singular; the dominant airflow in each season was primarily in the west and northwest; and the proportions were 71.62%, 66.85%, 61.22%, and 77.78%, respectively. There were certain seasonal differences in the source areas of O3 potential contribution:the high-value areas of O3 potential sources in spring, summer, and autumn were distributed in Baiyin City and Lanzhou City, which were southeast wind sources, and the high-value areas in winter were distributed between the Badain Jaran Desert and the Tengger Desert, which was the north wind source.

Preliminary Analysis of the Water Quality Status in an Urban Mediterranean River

Recreational use of urban rivers is becoming popular since rivers may act as amenity corridors with the potential for exhibition, recreation, leisure, relaxation and retreat. However, several point and non-point pollution sources contribute to the degradation of urban rivers’ water quality, thereby impeding their beneficial uses and amenities. The physicochemical and microbiological quality of a Greek urban river (Kifisos–Athens) was analyzed over a period of 12 months. A sampling campaign was implemented, collecting monthly data from five sites. Spearman’s analysis showed significant correlation of the Hellenic Water Quality Index with specific nutrients. The total physicochemical status of all sampling stations was characterized as poor or bad. The annual average concentration of Escherichia coli (E. coli) was extremely high in four sites out of five, ranging from 16,822 to 26,780 cfu/100 mL. Bacteriological quality was unacceptable, as the study demonstrated the widespread occurrence of E. coli and low-quality physiochemical conditions. The spatiotemporal distribution of pollution levels revealed hotspots to be monitored further via automatic monitoring stations. A series of management and restoration measures, including tracing the exact pollution routes, should be initiated to minimize pollution pressures and establish the good ecological status of an important Mediterranean river.

Analysis of Data on Air Pollutants in the City by Machine-Intelligent Methods Considering Climatic and Geographical Features

In the world, air pollution ranks among the primary sources of risk to human health and the environment. To assess the risk of impact of atmospheric pollution, a comprehensive research cycle was designed to develop a unified ecosystem for monitoring air pollution in industrial cities in Kazakhstan. Research involves analyzing data for the winter period from 20 automated monitoring stations (AMS) located in Almaty and conducting chemical-analytical studies of snowmelt water samples from 22 points to identify such pollutants as fine particulate matters, petroleum products, and heavy metals. Research includes a bio-experiment involving the cultivation of watercress on samples of melt water collected from snow cover to examine the effects of pollution on plants. In the framework of this research, we determined API based on data obtained from AMS. In order to determine the influence of atmospheric pollution on the environment, a multiple regression model was developed using machine learning algorithms to reveal the relationship between the bio-experiment data and data on pollutants of chemical-analytical research. The results revealed a wide spread of pollutants in the snow cover of the urban environment, a correlation between pollutants in the snow cover and the airspace of the city, and their negative impact on flora.

What do macroinvertebrate indices measure? Stressor-specific stream macroinvertebrate indices can be confounded by other stressors.

Monitoring programmes worldwide use biota to assess the "health" of water bodies. Indices based on biota are used to describe the change in status of sites over time, to identify progress against management targets and to diagnose the causes of biological degradation. A variety of numerical stressor-specific biotic indices have been developed based on the response of biota to differences in stressors among sites. Yet, it is not clear how variation in pressures within sites, over what time period, and in what combination has the greatest impact on different biotic groups. An understanding of how temporal variation in pressures influences biological assessment indices would assist in setting achievable targets and help focus catchment-scale mitigation strategies to ensure that they deliver the desired improvements in biological condition.Hydrochemical data provided by a network of high-frequency (15 or 30 min) automated monitoring stations over 3 years were matched to replicated biological data to understand the influence of spatio-temporal variation in pollution pressures on biological indices. Hydrochemical data were summarised in various ways to reflect central tendency, peaks, troughs and variation over 1-90 days before the collection of each biological sample. An objective model selection procedure was used to determine which hydrochemical determinand, and over what time period, best explained variation in the biological indices.Stressor-specific indices derived from macroinvertebrates which purportedly assess stress from low flows, excess fine sediment, nutrient enrichment, pesticides and organic pollution were significantly inter-correlated and reflected periods of low oxygen concentration, even though only one index (ASPTWHPT, average score per taxon) was designed for this purpose. Changes in community composition resulting from one stressor frequently lead to confounding effects on stressor-specific indices.Variation in ASPTWHPT was best described by dissolved oxygen calculated as Q5 over 10 days, suggesting that low oxygen events had most influence over this period. Longer-term effects were apparent, but were masked by recovery. Macroinvertebrate abundance was best described by Q95 of stream velocity over 60 days, suggesting a slower recovery in numbers than in the community trait reflected by ASPTWHPT.Although use of ASPTWHPT was supported, we recommend that additional independent evidence should be used to corroborate any conclusions regarding the causes of degradation drawn from the other stressor-specific indices. The use of such stressor-specific indices alone risks the mistargeting of management strategies if the putative stressor-index approach is taken to be more reliable than the results herein suggest.

Effects of Landscape Patterns on Atmospheric Particulate Matter Concentrations in Fujian Province, China

Based on the observation data from 37 national ambient air automatic monitoring stations and the land use/cover data in Fujian Province, the effects of land use/cover on the annual and seasonal variations in the air quality index (AQI), PM2.5 concentration and PM10 concentration are analyzed. In addition, buffer zones with different radii are established to calculate landscape pattern metrics and the influences of landscape patterns on the AQI, PM2.5 concentration and PM10 concentration at different scales are discussed. The results show that land use/cover types have remarkable impacts on the variations in the atmospheric particulate matter concentrations. The AQI and PM10 concentration are the highest for construction land, and the PM2.5 concentration is the highest for cultivated land. The seasonal variations in all air pollutant concentrations show similar characteristics, i.e., high in spring and winter and low in summer and autumn. Different landscape metrics have diverse effects on atmospheric particulate matter concentrations at different scales. In the buffer zone with a 5000 m radius, the patch number and patch density of forest land are positively correlated with the PM2.5 concentration, while the edge density of construction land has a negative correlation with it, indicating that landscape fragmentation affects the PM2.5 concentration. More fragmented forest land has a weaker effect on the reduction in the PM2.5 concentration, and more fragmented construction land has a weaker effect on the increase in the PM2.5 concentration. Moreover, the seasonal variations in the atmospheric particulate matter concentrations are different under different land use/cover types. Except for autumn, the AQI and PM2.5 concentration are most noticeably affected by forest land in all seasons, showing negative correlations. In autumn, the impacts of cultivated land on the AQI and PM2.5 concentration are more pronounced. The PM10 concentration is substantially affected by forest land in spring and summer and is markedly influenced by construction land in autumn and winter. The analysis of the landscape metrics of forest land and construction land at different scales indicates that the optimal scale is 5000 m for studying the annual average of the AQI and PM10 concentration and is 3000 m for investigating the annual average of the PM2.5 concentration. The optimal scales to research the seasonal variations in the AQI, PM2.5 and PM10 concentrations are 4000–5000 m for forest land and construction land, while the optimal scale is 1000 m for cultivated land to research the AQI and PM2.5 in autumn. This study can provide a scientific basis for the optimization of land use/cover and landscape patterns in Fujian Province, the planning and management of green space and the selection of research scales in the future.

High spatial resolution assessment of air quality in urban centres using lichen carbon, nitrogen and sulfur contents and stable-isotope-ratio signatures

Air pollution and poor air quality is impacting human health globally and is a major cause of respiratory and cardiovascular disease and damage to human organ systems. Automated air quality monitoring stations continuously record airborne pollutant concentrations, but are restricted in number, costly to maintain and cannot document all spatial variability of airborne pollutants. Biomonitors, such as lichens, are commonly used as an inexpensive alternative to assess the degree of pollution and monitor air quality. However, only a few studies combined lichen carbon, nitrogen and sulfur contents, with their stable-isotope-ratio signatures (δ13C, δ15N and δ34S values) to assess spatial variability of air quality and to ‘fingerprint’ potential pollution sources. In this study, a high-spatial resolution lichen biomonitoring approach (using Xanthoria parietina and Physcia spp.) was applied to the City of Manchester (UK), the centre of the urban conurbation Greater Manchester, including considerations of its urban characteristics (e.g., building heights and traffic statistics), to investigate finer spatial detail urban air quality. Lichen wt% N and δ15N signatures, combined with lichen nitrate (NO3−) and ammonium (NH4+) concentrations, suggest a complex mixture of airborne NOx and NHx compounds across Manchester. In contrast, lichen S wt%, combined with δ34S strongly suggest anthropogenic sulfur sources, whereas C wt% and δ13C signatures were not considered reliable indicators of atmospheric carbon emissions. Manchester’s urban attributes were found to influence lichen pollutant loadings, suggesting deteriorated air quality in proximity to highly trafficked roads and densely built-up areas. Lichen elemental contents and stable-isotope-ratio signatures can be used to identify areas of poor air quality, particularly at locations not covered by automated air quality measurement stations. Therefore, lichen biomonitoring approaches provide a beneficial method to supplement automated monitoring stations and also to assess finer spatial variability of urban air quality.Graphical abstract

Evaluating TROPOMI and MODIS performance to capture the dynamic of air pollution in São Paulo state: A case study during the COVID-19 outbreak

Atmospheric pollutant data retrieved through satellite sensors are continually used to assess changes in air quality in the lower atmosphere. During the COVID-19 pandemic, several studies started to use satellite measurements to evaluate changes in air quality in many different regions worldwide. However, although satellite data is continuously validated, it is known that its accuracy may vary between monitored areas, requiring regionalized quality assessments. Thus, this study aimed to evaluate whether satellites could measure changes in the air quality of the state of São Paulo, Brazil, during the COVID-19 outbreak; and to verify the relationship between satellite-based data [Tropospheric NO2 column density and Aerosol Optical Depth (AOD)] and ground-based concentrations [NO2 and particulate material (PM; coarse: PM10 and fine: PM2.5)]. For this purpose, tropospheric NO2 obtained from the TROPOMI sensor and AOD retrieved from MODIS sensor data by using the Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm were compared with concentrations obtained from 50 automatic ground monitoring stations. The results showed low correlations between PM and AOD. For PM10, most stations showed correlations lower than 0.2, which were not significant. The results for PM2.5 were similar, but some stations showed good correlations for specific periods (before or during the COVID-19 outbreak). Satellite-based Tropospheric NO2 proved to be a good predictor for NO2 concentrations at ground level. Considering all stations with NO2 measurements, correlations >0.6 were observed, reaching 0.8 for specific stations and periods. In general, it was observed that regions with a more industrialized profile had the best correlations, in contrast with rural areas. In addition, it was observed about 57% reductions in tropospheric NO2 throughout the state of São Paulo during the COVID-19 outbreak. Variations in air pollutants were linked to the region economic vocation, since there were reductions in industrialized areas (at least 50% of the industrialized areas showed >20% decrease in NO2) and increases in areas with farming and livestock characteristics (about 70% of those areas showed increase in NO2). Our results demonstrate that Tropospheric NO2 column densities can serve as good predictors of NO2 concentrations at ground level. For MAIAC-AOD, a weak relationship was observed, requiring the evaluation of other possible predictors to describe the relationship with PM. Thus, it is concluded that regionalized assessment of satellite data accuracy is essential for assertive estimates on a regional/local level. Good quality information retrieved at specific polluted areas does not assure a worldwide use of remote sensor data.

Characterization of Atmospheric Particulate Matter in Oil-Resource Cities and its Impact on Health: Daqing City, Northeast China

Atmospheric pollution can affect human production life and physical and mental health to a great extent. In this paper, the hourly pollutant monitoring data from five state-controlled automatic atmospheric monitoring stations in Daqing City from 2017 to 2021 are collected to analyze the temporal and spatial variation patterns of CO, SO2, NO2, PM2.5, PM10, O3. And to assess the health risks of atmospheric particulate matter. The HYSPLIT model is applied to analyze air pollutants' transport pathways and the potential source areas of atmospheric particulate matter with the PSCF model. The results show that the concentrations of SO2 and NO2 are higher in the north of Daqing and the concentrations of pollutants generally show a decreasing trend with time. The hazard quotient (HQ) for atmospheric particulate matter, which is slightly above the safe range set by the EPA, is higher in winter and spring. After principal component analysis, CO, NO2, and PM10 are the main factors affecting PM2.5. In the summer, the main urban area's air pollution is mostly influenced by the southwest pollution trajectory. In other seasons, the northwest route predominantly regulates the regional transfer of contaminants. The key regions that could be the sources of atmospheric particulate matter include North China, Inner Mongolia, Mongolia, and Russia.