Concentrations Of Various Pollutants Research Articles

Response of aquatic ecosystems multi-trophic biological communities under multiple pollutants stress

Industrialization has significantly polluted the Yangtze River Basin, posing phenolic compounds and heavy metals that threaten ecological and human health. This study comprehensively evaluated the impact of these pollutants on the Yangtze River's aquatic ecosystems across multiple trophic levels. Sampling from 84 sites during both dry and wet seasons, water chemistry and biological data were analyzed by advanced molecular and statistical techniques. All organisms are divided into three ecological functional groups with a multitrophic level structure based on their co-occurrence. Our results demonstrate pronounced spatial and seasonal variations in pollutant concentrations, with notable sensitivity in ecological structures to phenolic compounds and heavy metals. The ecological impacts of pollutants are more readily observable (up to 43.1 % maximum) when assessing ecological communities based on functional groups with multi-trophic biological community as opposed to traditional taxonomic classifications. All the data used to develop a predictive model for ecological functional groups, achieving an accuracy rate of 86.1 %. This research provides critical insights into the multi-trophic effects of composite pollution, advocates for the adoption of comprehensive multi-trophic evaluation methods in large-scale ecological monitoring and sustainable watershed management globally.

Investigating the trend of the vertical distribution of ambient air pollutants (PM2.5, PM10, NOX, and NO2); A systematic review

BackgroundNumerous investigations have explored variations in vertical air pollutant concentrations, yielding diverse findings. So, we have conducted this systematic review to gain a more comprehensive understanding of the vertical distribution of air pollutant concentrations (PM2.5, PM10, NOX, and NO2) and the influencing factors. MethodsRelevant studies were identified by searching the three central electronic databases, PubMed, Web of Science, and Scopus, from the beginning of 2000 to the end of 2023. This study included original articles published in English that examine the pollutant concentration variations below 500 m. FindingOf 3614 articles, 57 studies met our criteria. Our findings showed a decline in PM2.5 and NOX concentrations as altitude increased, while NO2 concentrations exhibited an increase. Conversely, no statistically significant relationship was identified between altitude and PM10 concentrations. The regression analysis yielded coefficients for the relationship between concentration and altitude (0-500 m) as follows: PM2.5 (-0.11), PM2.5/PM10 (0.0008), NOX (-0.11), and NO2 (0.13). Conducting additional research on this topic and investigating the impact of meteorological parameters on pollutant concentrations at different altitudes enhances our understanding of the vertical distribution of pollutant concentrations.

Spatial Analysis of Air Pollutants in an Industrial City Using GIS-Based Techniques: A Case Study of Pavlodar, Kazakhstan

The given research employs high-resolution air quality monitoring and contemporary statistical methods to address gaps in understanding the urban air pollution in Pavlodar, a city with a significant industrial presence and promising touristic potential. Using mobile air quality sensors for detailed spatial data collection, the research aims to quantify concentrations of particulate matter (PM2.5, PM10), carbon monoxide (CO), nitrogen dioxide (NO2), sulfur dioxide (SO2), and ground-level ozone (O3); assess their distribution; and identify key influencing factors. In this study, we employed Geographic Information Systems (GISs) for spatial analysis, integrating multi-level B-spline interpolation to model spatial variability. Correlation analysis and structural equation modeling were utilized to explore the relationships between variables, while regression analysis was conducted to quantify these relationships. These techniques were crucial for accurately mapping and interpreting spatial patterns and their underlying factors. The study identifies PM2.5 and NO2 as the primary contributors to air pollution in Pavlodar, with NO2 exceeding the 24 h threshold in 87.38% of locations and PM2.5 showing the highest individual air quality index (AQI) in 75.7% of cases. Correlation analysis reveals a positive association between PM2.5 and AQI and a negative correlation between NO2 and AQI, likely due to the dominant influence of PM2.5 in AQI calculations. Structural equation modeling (SEM) further underscores PM2.5 as the most significant impactor on AQI, while NO2 shows no significant direct impact. Humidity is positively correlated with AQI, though this relationship is context-specific to seasonal patterns observed in May. The sectoral analysis of landscape indices reveals weak correlations between the green space ratio (GSR) and air quality, indicating that while vegetation reduces pollutants, its impact is minimal due to urban planting density. The road ratio (RR) lacks sufficient statistical evidence to draw conclusions about its effect on air quality, possibly due to the methodology used. Spatial variability in pollutant concentrations is evident, with increasing PM2.5, PM10, and AQI towards the east-northeast, likely influenced by industrial activities and prevailing wind patterns. In contrast, NO2 pollution does not show a clear geographic pattern, indicating vehicular emissions as its primary source. Spatial interpolation highlights pollution hotspots near industrial zones, posing health risks to vulnerable populations. While the city’s overall AQI is considered “moderate”, the study highlights the necessity of implementing measures to improve air quality in Pavlodar. This will not only enhance the city’s attractiveness to tourists but also support its sustainable development as an industrial center.

Multiscale CFD analysis of urban air pollution dome and ventilation enhancement via an urban chimney

Air pollution episodes are critical environmental challenges in urban areas, primarily linked to the lack of ventilation during calm and stable atmospheric conditions, rather than a significant increase in emissions. The prevailing circulation pattern under these conditions is the Urban Heat Island Circulation (UHIC), which governs the characteristics of the urban pollution dome and the distribution of pollutants. This study aims to assess the UHIC's influence on spatial and temporal variations in pollutant concentrations and to investigate the efficiency of an Urban Chimney (UC) in improving air quality. Due to the interaction of micro- and meso-scale flows, a multi-scale analysis is required. For this purpose, a pressure-based CFD solver is adapted for analyzing airflow and pollutant dispersion in a multi-scale environment. A coordinate transformation method is used to account for meso-scale effects, and the resulting source terms are applied to the solver. The species transport equation is transformed to the new coordinate system, and the effects of species diffusion on enthalpy transport are included in the governing equations. The results demonstrate that UHIC reduces the average pollutant concentration and causes pollution peaks near ground level and at the city center. The interaction of micro-scale flow within the chimney with meso-scale UHIC impacts the flow field and pollutant concentration across the entire domain, decreasing the urban plume's vertical velocity by 40 percent and the mixing height at the city center by 20 percent. It also increases the vertical velocity within the UC by 23 percent. The UC can serve as a controlling tool for urban ventilation, and its capacity to remove pollutants increases sixfold when interacting with UHIC, reducing pollutant concentration citywide.

Air Quality Index Assessment Of An Educational Organization

Abstract Uncontaminated and fresh air is an elementary prerequisite of every living being. But, in this day and age, atmosphere contamination-is certainly the most pervasive kind of environmental-degradation. In the ambience, there are several pollutants which have widespread causing contamination in the air. Pollutants of chief concern include gaseous pollutants like carbon monoxide, ozone, oxides of sulphur and nitrogen, lead etc. These are primary pollutants. Secondary pollutants are particulate matters of different types ranging from fine to high. Every single one of these pollutants can cause a severe menace to human-health. In the current research, sensor-based equipments have been used to monitor and measure the concentration of various pollutants in the air. In the present research work, the effect of different pollutants like PM2.5, PM10, NO2 C02, TVOC, H-CHO, Temperature, Humidity and Noise on air pollution at five stations in the education institute namely RCOEM campus is studied and presented. It had been observed that air pollutants like PM2.5, PM10, and NO2 primarily affected the air quality of RCOEM campus. So, these pollutants are mainly considered to monitor and evaluate air quality of RCOEM campus. After evaluation, air quality index of RCOEM campus was found to be around 94.5. It is within the range of 51-100. So, its falls in the range of satisfactory as per standard norms of central pollution control board and state pollution control board in terms of quality of the air and thereby its contamination.

«Lace Miracle», lichens as an indicator of the ecological situation using the example of Turkey Creek, Florida

In this article, we took a walk-exploration of a piece of paradise called Turkey Creek in the center of Florida. Enjoying the spring period of the awakening of nature, accompanied by a multitude of birds and other unique sounds of the inhabitants of Turkey Creek, we paid special attention to the diversity of lichens, which, as bioindicators, react and reflect the state and quality of the environment. Lichens are very sensitive to air pollution and die when there is a high content of carbon monoxide, sulfur compounds, nitrogen and fluorine. The degree of sensitivity varies among species, so they can be used as living indicators of environmental cleanliness. Based on the composition of lichens, the concentration of various pollutants in the air can be quantified using developed scales and formulas. Lichens are a unique group of lower plants. Lichens combine two organisms with opposite properties: an alga (usually green), which creates organic matter through the process of photosynthesis, and a fungus that consumes this substance. Lichens are characterized by fairly high tolerance to climatic factors and sensitivity to environmental pollutants and are widely used as environmental indicators. Lichens contain many macro- and microelements, organic acids, hormones and enzymes. Vitamins are produced by lichens in very small quantities. Lichens produce specific substances that are not found in other organisms (lichen starch, lichen acids, etc.). Lichens have the ability to extract various elements from the environment and accumulate in their thallus. The number of currently known lichen substances exceeds 250, of which 75 are found only in lichens. Lichens are indicators of the environmental situation. Our goal is to find out whether recreational load affects the state of the environment in people’s recreation areas. We carried out our study of the state of the environment in the state of Florida in the Turkey Creek natural park. This territory was chosen because the natural complexes of the area are characterized by high environment-forming, environmental, and recreational significance. In the last decade, the anthropogenic load on ecosystems associated with the recreational use of the territory has been increasing. It is worth familiarizing yourself with the types of lichens that were seen in the study area.

Urban air pollution evaluation in downtown streets of a medium-sized Latin American city using AERMOD dispersion model.

The transport sector is considered the largest contributor of air pollutants in urban areas, mainly on-road vehicles, affecting the environment and human health. Bahía Blanca is a medium-sized Latin American city, with high levels of traffic in the downtown area during peak hours. In this regard, it is necessary to analyze air pollution using an air quality model considering that there are no air pollutant measurements in the central area. Furthermore, this type of study has not been carried out in the region and since the city is expected to grow, it is necessary to evaluate the current situation in order to make effective future decisions. In this sense, the AERMOD model (US-EPA version) and the RLINE source type were used in this work. This study analyzes the variations of pollutant concentrations coming from mobile sources in Bahía Blanca's downtown area, particularly carbon monoxide (CO) and nitrogen oxides (NOx) during the period Jul-2020 to Jun-2022. It is interesting to note the results show the maximum concentration values detected are not directly associated with maximum levels of vehicle flow or emission rates, which highlights the importance of meteorological parameters in the modeling. In addition, alternative scenarios are proposed and analyzed from a sustainable approach. Regarding the scenario analysis, it can be concluded that diesel vehicles have a large influence on NOx emissions. Moreover, restrictions as strict as those proposed for a Low Emission Zone would be less applicable in the city than alternative temporary measures that modify traffic at peak hours.

Long-term variations of air pollutants and public exposure in China during 2000–2020

Since 2000, China has faced severe air pollution challenges，prompting the initiation of comprehensive emission control measures post-2013. The subsequent implementation of these measures has led to remarkable enhancements in air quality. This study aims to enhance our understanding of the long-term trends in fine particulate matter (PM2.5) and gaseous pollutants of ozone (O3) and nitrogen dioxide (NO2) across China from 2000 to 2020. Utilizing the Community Multiscale Air Quality (CMAQ) model, we conducted a nationwide analysis of air quality, systematically quantifying model predictions against observations for pollutants. The CMAQ model effectively captured the trends of air pollutants, meeting recommended performance benchmarks. The findings reveal variations in pollutant concentrations, with initial increases in PM2.5 followed by a decline after 2013. The proportion of the population living in high PM2.5 concentrations (>75 μg/m3) decreased to <5 % after 2015. However, during the period from 2017 to 2020, around 40 % of the population continued to live in regions that did not meet the criteria for Chinese air quality standards (35 μg/m3). From 2000 to 2019, fewer than 20 % of the population met the WHO standard (100 μg/m3) for MDA8 O3. In 2000, 77 % of the population met the NO2 standard (<20 μg/m3), a figure that declined to 60 % between 2005 and 2014, nearly reaching 70 % in 2020. This study offers a comprehensive analysis of the changes in pollutants and public exposure in 2000–2020. It serves as a foundational resource for future efforts in air pollution control and health research.

Spatio-temporal evaluation of air pollution using ground-based and satellite data during COVID-19 in Ecuador

The concentration of gases in the atmosphere is a topic of growing concern due to its effects on health, ecosystems etc. Its monitoring is commonly carried out through ground stations which offer high precision and temporal resolution. However, in countries with few stations, such as Ecuador, these data fail to adequately describe the spatial variability of pollutant concentrations. Remote sensing data have great potential to solve this complication. This study evaluates the spatiotemporal distribution of nitrogen dioxide (NO2) and ozone (O3) concentrations in Quito and Cuenca, using data obtained from ground-based and Sentinel-5 Precursor mission sources during the years 2019 and 2020. Moreover, a Linear Regression Model (LRM) was employed to analyze the correlation between ground-based and satellite datasets, revealing positive associations for O3 (R2 = 0.83, RMSE = 0.18) and NO2 (R2 = 0.83, RMSE = 0.25) in Quito; and O3 (R2 = 0.74, RMSE = 0.23) and NO2, (R2 = 0.73, RMSE = 0.23) for Cuenca. The agreement between ground-based and satellite datasets was analyzed by employing the intra-class correlation coefficient (ICC), reflecting good agreement between them (ICC ≥0.57); and using Bland and Altman coefficients, which showed low bias and that more than 95% of the differences are within the limits of agreement. Furthermore, the study investigated the impact of COVID-19 pandemic-related restrictions, such as social distancing and isolation, on atmospheric conditions. This was categorized into three periods for 2019 and 2020: before (from January 1st to March 15th), during (from March 16th to May 17th), and after (from March 18th to December 31st). A 51% decrease in NO2 concentrations was recorded for Cuenca, while Quito experienced a 14.7% decrease. The tropospheric column decreased by 27.3% in Cuenca and 15.1% in Quito. O3 showed an increasing trend, with tropospheric concentrations rising by 0.42% and 0.11% for Cuenca and Quito respectively, while the concentration in Cuenca decreased by 14.4%. Quito experienced an increase of 10.5%. Finally, the reduction of chemical species in the atmosphere as a consequence of mobility restrictions is highlighted. This study compared satellite and ground station data for NO2 and O3 concentrations. Despite differing units preventing data validation, it verified the Sentinel-5P satellite's effectiveness in anomaly detection. Our research's value lies in its applicability to developing countries, which may lack extensive monitoring networks, demonstrating the potential use of satellite technology in urban planning.

Evaluating Drinking Water Quality in Salem District Using the DEMATEL Method

Access to safe drinking water is a fundamental human right and crucial for maintaining a healthy life. This study focuses on the analysis of drinking water quality in the Salem district, assessing the concentration of various pollutants and their associated health risks. Water samples were collected from bore wells, tube wells, and hand pumps. Major sources of contamination identified include the improper disposal of sewage and solid waste, excessive use of agrochemicals, and the poor condition of the piping and transportation network. Water pollution, particularly contamination with coliform bacteria, is linked to water-borne diseases such as gastroenteritis, diarrhea, dysentery, and viral hepatitis. These contaminants are a significant cause of health issues. To mitigate health risks, it is crucial to immediately stop using contaminated drinking water sources and limit the excessive use of agricultural chemicals that contribute to water pollution. The current study also aims to examine the factors influencing the selection of supply chain management (SCM) suppliers, utilizing the Neutrosophic Decision-Making and Evaluation Laboratory (DEMATEL) method. A proactive approach was adopted to enhance DEMATEL's performance and achieve a competitive edge, using neutrosophic set theory to mark values on a new scale. A case study implementing this method is presented, which involved collecting data through interviews with experts on the Neutrosophic DEMATEL model. The research is intended for use in management, procurement, and production contexts. In terms of drinking water quality, the study found that R+C Omalur ranked highest, while Sankari ranked lowest. Conversely, Ri-C Sankari scored the highest, with Omalur ranking the lowest in another drinking water quality metric.

Variation in urban snow quality indicated by three seasonal sampling surveys conducted in Luleå (Sweden) within a span of 27 years

Concentrations of total suspended solids (TSS), trace metals (Cu, Cd, Cr, Zn, Cd, Pb), Na and 16 US EPA priority PAHs in urban snow were studied in the City of Luleå in Northern Sweden. Snow was sampled at six central urban and suburban sites with various traffic intensities, in three sampling surveys (1994–95, 2002–03, 2020–21), repeated for three ages of the urban snow cover of 40, 80, and 120 days, respectively. The older data, from the 1994–95 and 2002–03 surveys, were obtained from the existing literature. The concentrations and mass loads of TSS and most trace metals studied (Zn, Cu, Pb, and Cd) varied with time. TSS, Zn, and Cu showed slightly higher concentrations and mass loads in the 2003 (TSS avg = 2300 μg/L, Zn avg = 620 μg/L and Cu avg = 250 μg/L) and 2021 (TSS avg = 1500 μg/L, Zn avg = 530 μg/L and Cu avg = 220 μg/L) sampling surveys, compared to the 1995 survey (TSS avg = 620 μg/L, Zn avg = 240 μg/L and Cu avg = 97 μg/L). However, no evident trend was observed between the 2003 and 2021 sampling surveys. The highest concentrations of Pb and Cd were observed in snow samples from the 1994–95 sampling survey (Pb max = 570 μg/L, Cd max = 4.6 μg/L). Results indicated higher concentrations of the pollutants studied in the city centre, compared to the residential suburbs, and in areas with heavier traffic, where concentrations of metals correlated well with traffic intensity. Fractionation analysis of trace metals indicated that Zn, Cu and Pb occurred mostly in the particulate-bound phase (>0.45 μm) containing the most of Zn, Cu, and Pb mass, at 80, 84 and 94% of the total, respectively. Over 50% of the dissolved phase of Zn and Cu was in the truly dissolved fraction (<3000 MWCO). Concentrations of PAHs also increased with traffic intensity, with pyrene being the most frequently detected PAH, likely because of the strength of sources and various physical processes influencing the snowbanks development and causing spatial and temporal variations in pollutant concentrations.

Characterization of summertime single aerosol particles in Chengdu (China): Interannual evolution and impact of COVID-19 lockdown

To investigate the interannual evolution of air pollution in summer and the impact of the COVID-19 lockdown on local pollution in Chengdu, China, single aerosol particles were continuously measured in three summer periods: the regular period in 2020 (RP2020); the regular period in 2022 (RP2022); and the lockdown period in 2022 (LP2022). It was found that, from RP2020 to RP2022, the mass concentrations of PM2.5, PM10, SO2 and NO2 decreased by 25.6 %, 24.7 %, 28.8 % and 38.5 %, respectively, while the concentration of O3 increased by 11.0 %. Affected by regional transport, there was no significant decrease in the concentrations of various pollutants during LP2022. All single aerosol particles could be classified into seven categories: vehicle emissions (VE), dust, biomass burning (BB), coal combustion (CC), K mixed with sulfate (KSO4), K mixed with nitrate (KNO3) and K mixed with sulfate and nitrate (KSN) particles. From RP2020 to RP2022, the contributions of BB and CC particles decreased by 12.1 % and 0.9 %, respectively, while VE and dust particles increased by 3.6 % and 2.5 %, respectively; and compared to RP2022, the contributions of VE, dust and CC particles in LP2022 decreased by 22.2 %, 11.0 % and 12.7 %, respectively. The high PM2.5 pollution events in RP2020 and RP2022 were mainly caused by combustion sources (BB and CC, 51.6 %) and VE (38.3 %) particles, respectively, while the pollution event in LP2022 was contributed by BB (27.0 %) and secondary inorganic (KSO4, KNO3 and KSN, 60.2 %) particles. The formation mechanisms of different pollution events were further validated by WRF-Chem results. Although the potential source areas of particles showed a shrinking trend from RP2020 to RP2022, regional transport still caused high PM2.5 pollution events during LP2022. Photochemical processes dominated the formation of KSO4 particles, while the KNO3 and KSN particles were mainly generated by liquid-phase reactions, and this effect increased year by year.

Health and economic benefits of heavy-duty diesel truck emission control policies in Beijing

PM2.5 emissions from heavy-duty diesel trucks (HDDTs) have a significant impact on air quality, human health, and climate change, and seriously threaten the UN Sustainable Development Goals. Globally, a series of emission control measures have been implemented to reduce pollution emissions from HDDTs. Current studies assessing the impact of these measures on air quality and human health have mainly used coarse-grained emission data as input to dispersion model, resulting in the inability to capture the spatiotemporal variability of pollutant concentrations and tending to increase the uncertainty of health impact assessment results. In this study, we quantified the impact of pollution control policies for HDDTs in Beijing on PM2.5 concentrations, human health, and economic losses by integrating policy scenario analysis, pollution dispersion simulation, public health impact and economic benefit assessment models, supported by high spatiotemporal resolution emission data from HDDTs. The results show that PM2.5 concentrations from HDDTs exhibit significant spatial aggregation characteristics, with the intensity of aggregation at night being about twice as high as that during the day. The emission hotspots are mainly concentrated in the sixth, fifth and fourth rings and major highways. Compared to the “business as usual” scenario in 2018, the current policy of updating the fuel standard to China VI and the emission standard to China 6 can reduce PM2.5 concentrations by 96.72%, thereby avoiding 612 premature deaths, which is equivalent to obtaining economic benefits of 1.65 billion CNY. This study further emphasizes the importance of high spatiotemporal resolution emission data during traffic dispersion modeling. The results can help improve the understanding of the effectiveness of emission reduction measures for HDDTs from a health benefit perspective.

Measurements of traffic-related air pollution at a U.S.–Mexico port of entry and its impacts on nearby community

Traffic-related air pollutants are especially prevalent in border cities with multiple ports of entry (POEs). Excessive emissions due to long delays of idling commercial and passenger vehicles at the POEs could exacerbate various types of respiratory health conditions for the POE users and workers. We conducted a 1-month in-traffic air monitoring campaign at a POE in El Paso, Texas using three continuous FEM instruments for three criteria pollutants (PM, O3, and NO2). Concurrent monitoring of PM in the nearby community was performed using a number of low-cost PM sensors. The 1-month average PM2.5 concentration at the POE was found below 11.6 µg/m3, while the 5-min average of in-traffic PM2.5 concentration could be as high as 450 µg/m3. The in-traffic PM2.5 concentrations were comparable to the data concurrently measured in a state-operated community monitoring site approximately 0.4 km away from the POE. Ozone (O3) concentrations remained low in the region. The in-traffic 1-h NO2 concentrations were below the 1-h NAAQS of 100 ppb, except for only two 5-min intervals. Our results suggest that traffic emissions do not result in elevated levels of pollutant concentrations at this POE compared to the concentration levels observed in the nearby community. In addition, the performance and accuracy of the low-cost sensors appear to be less reliable during our study, although the devices were capable of detecting the trends and variability in pollutant concentrations in real time.

City Wind Impact on Air Pollution Control for Urban Planning with Different Time-Scale Considerations: A Case Study in Chengdu, China

Economic development and fast growing urbanization in China have caused severe air pollution, with frequent pollution episodes endangering the health of inhabitants and disturbing social activities, and as an expanding metropolis, Chengdu has suffered ever since. The concentration variations of main air pollutants, such as PM10, PM2.5 and NO2, often show periodicity because of meteorological impact and anthropic activities, and display orientation discrepancies due to influences of wind speed (WS), frequency and pollutant sources. These features have complicated the mechanisms of pollution episodes and deepened the difficulty in pollution control evaluation. The WS has significant influences on the periodicity and orientation variations in pollutant concentrations, and quantifying the influence of which is of high significance and provides sustainable foundations for pollution alleviation strategies. Different time-scale cycles (i.e., Diurnal, weekly, seasonal and annual), along with the WS, wind frequency, wind and spatial orientations in urban areas, were analyzed in this paper. Results show that the periodicity of diurnal, seasonal and annual cycles is remarkable, and weekly cycle is obvious by adding the influence of the WS in 16 orientations. The WS has direct impacts on pollutants varying in the range of 1.5–2.5 m/s, and has a remarkable diffusion effect on pollutants once above 2.5 m/s. Over heavy pollution hours in diurnal, weekly, annual cycles and transitional seasons, the WS had more significant influences on pollutants, and whereas the wind frequency is not the main impact factor for orientation variations. For Chengdu, the northeast orientation is suitable to construct a wind panel with a remarkable diffusion effect on pollutants, while air pollutions in the northwest and southwest orientations were severe with the WS below 1.5 m/s, and pollution diffusion in the north-northwest orientation was the worst. This work can provide guidance and reference for urban planning optimization and air environment protection in cities with air quality control considerations impacted by city wind.

Spatiotemporal Assessment of Atmospheric Pollutants in Yancheng City, Eastern Coastal City of China

Atmospheric environmental pollution has become a critical issue in eastern coastal cities in China, so a broad understanding of its spatiotemporal characteristics is of importance to develop public policies. In this study, hourly data of ρ(PM2.5), ρ(PM10), ρ(NO2), ρ(SO2), ρ(O3) and φ(CO) of five different types of national air quality monitoring sites from 2016 to 2020 were analyzed, combined with the change of meteorological elements in the same period in Yancheng, which was a rapidly developed eastern coastal city in China. The results indicated that the pollutant concentrations except for ρ(O3) was low in summer and high in winter, decreasing year by year from 2016 to 2020. The proportion of moderately and heavily contaminated days in the whole year was decreasing from 80 days in 2016 to 52 days in 2020, and the days with good quality increased from 284 days in 2016 to 311 days in 2020. ρ(O3) was the highest in spring and the lowest in winter, increasing slightly year by year. The variation of ρ(PM2.5), ρ(PM10), ρ(NO2), ρ(SO2) and φ(CO) showed a double-peak type, reaching the peak value at 8:00–10:00 and 20:00–22:00, corresponding to the early and evening rush hours. ρ(PM2.5), ρ(PM10) and φ(CO) on the weekend were higher than on weekdays, while an insignificant difference of ρ(NO2), ρ(O3) and ρ(SO2) was found between weekdays and the weekend. Wind direction played a key role in the variation of pollutant concentration in the Yancheng urban area, and the correlation analysis indicated that ρ(PM2.5) and ρ(PM10) were highly correlated to wind direction. Temperature was positively correlated to ρ(O3), while air pressure was significantly negatively correlated to ρ(O3). Relative humidity was negatively correlated to ρ(PM2.5), ρ(PM10), ρ(NO2), ρ(SO2) and φ(CO), while air pressure was positively correlated with these pollutants.

Air pollutant prediction based on ARIMA-WOA-LSTM model

The problem of air pollution has always plagued people's lives, and the management of air pollution cannot be achieved without the prediction and assessment of the concentration of various pollutants. In this paper, we propose a method to accurately predict air pollutants with the aim of ensuring the efficiency of air pollution management. The proposed ARIMA-WOA-LSTM model uses ARIMA to extract the linear part of air pollution data and output the nonlinear part, while the WOA-LSTM model is used to predict the nonlinear part, where the whale algorithm is used to find the perfect hyperparameters for the LSTM, and the objectives of the search include the number of neurons, model learning rate and batch length. To prove the excellence of the model developed in the article ARIMA-WOA-LSTM is compared with ARIMA-LSTM, CEEMDAN-WOA-LSTM, WOA-LSTM, ARIMA, and LSTM. The results show that ARIMA-WOA-LSTM performs better than other models in three aspects: pollutant prediction accuracy, model prediction accuracy, and prediction stability; the combined model also performs much better than the single model in the three aspects; The whale algorithm is excellent for the search of the five hyperparameters in the LSTM, which is important for the error reduction of the model. ARIMA-WOA-LSTM model has high reference for air pollution management.

First evidence and potential sources of novel brominated flame retardants and BDE 209 in the deepest ocean

Organic anthropogenic pollutants reach even the deepest parts of the oceans, i.e., the hadal trenches. We here presented the concentrations, influencing factors, and potential sources of polybrominated diphenyl ethers (PBDEs) and novel brominated flame retardants (NBFRs) in hadal sediments and amphipods from the Mariana, Mussau and New Britain trenches. Results showed that BDE 209 was the dominant PBDEs congener and DBDPE was the dominant NBFRs. No significant correlation was found between TOC contents and PBDEs or NBFRs levels in sediment. Lipid content and body length were the potential important factors affecting variation in pollutant concentrations in the carapace & muscle of amphipods, while the pollution levels of viscera were mainly affected by the sex and lipid content. PBDEs and NBFRs might reach trench surface seawater through long-range atmospheric transport and oceans currents but with little contribution from the Great Pacific Garbage Patch. Determination of carbon and nitrogen isotopes indicated that the pollutants were transported and accumulated in amphipods and sediment via different pathways. PBDEs and NBFRs in the hadal sediments were generally transported via the settling of sediment particles of either marine or terrigenous origin whereas in amphipods they accumulated via feeding on animal carrion through the food web. This is the first study reporting on BDE 209 and NBFR contaminations in hadal settings and provide new insight on influencing factors and sources of PBDEs and NBFRs in the deepest oceans.

Understanding Responses of Atmospheric Pollution and its Variability to Contradicting Nexus of Urbanization–Industrial Emission Control in Haldia, an Industrial City of West Bengal

Poor atmospheric health has been causing millions of deaths globally by attacking the respiratory system. According to the World Health Organization, India has the most heavily polluted cities overall and contributes significantly to the annual particle concentration at a global level. This situation demands intensive and extensive monitoring systems for strategy optimization at various levels of governance to combat health and environmental. These strategies include an efficient air quality control strategy, the management of pollution sources while taking into account both local and national controls, etc. However, inadequacy in monitoring stations, which are primarily targeted at cities, has kept most of the small towns and villages out of the radar. This poses difficulty in assessing pollution levels at the local and regional scales, thereby causing a poor understanding of the intensity and spatio-temporal variability of pollutant concentration. Addressing this issue, an attempt has been made in this study to understand the atmospheric pollution of an industry-based city, Haldia in West Bengal, where all the monitoring stations are situated in industrial centers and function nearby making a larger part unmonitored. Handheld instruments were used to collect field data for five other stations selected mainly from non-industrial areas experiencing rapid detrimental land use conversion. Despite being spatially consistent in higher (2015–2019 mean of 95) and lower Air Quality Index (2015–2019 mean of 65), respectively, in industrial and non-industrial areas, contradicting temporal trend is quite distinct in them. Pollution has increased in areas experiencing rapid LULC change, especially in rapidly urbanized areas, whereas industrial emission controls and implementation of various pollution control strategies, including implementation of pollution base profiling and baseline policy action, graded response action plan, etc., have been successful in abating pollution in areas near industries. The study pinpoints the requirement of spatially consistent awareness in rapidly urbanized sections of Haldia to avoid possible environmental hazards caused by urbanization and the necessity of having a dense network of monitoring stations to capture local variations in pollutant concentrations for a better understanding of atmospheric health.

A method for calibrating measurement data of a micro air quality monitor based on MLR-BRT-ARIMA combined model.

A micro air quality monitor can realize grid monitoring and real-time monitoring of air pollutants. Its development can effectively help human beings to control air pollution and improve air quality. However, affected by many factors, the measurement accuracy of micro air quality monitors needs to be improved. In this paper, a combined calibration model of Multiple Linear Regression, Boosted Regression Tree and AutoRegressive Integrated Moving Average model (MLR-BRT-ARIMA) is proposed to calibrate the measurement data of the micro air quality monitor. First, the very widely used and easily interpretable multiple linear regression model is used to find the linear relationship between various pollutant concentrations and the measurement data of the micro air quality monitor to obtain the fitted values of various pollutant concentrations. Second, we take the measurement data of the micro air quality monitor and the fitted value of the multiple regression model as the input, and use the boosted regression tree to find the nonlinear relationship between the concentrations of various pollutants and the input variables. Finally, the autoregressive integrated moving average model is used to extract the information hidden in the residual sequence, and finally the establishment of the MLR-BRT-ARIMA model is completed. Root mean square error, mean absolute error and relative mean absolute percent error are used to compare the calibration effect of the MLR-BRT-ARIMA model and other commonly used models such as multilayer perceptron neural network, support vector regression machine and nonlinear autoregressive models with exogenous input. The results show that no matter what kind of pollutant, the MLR-BRT-ARIMA combined model proposed in this paper has the best performance of the three indicators. Using this model to calibrate the measurement value of the micro air quality monitor can improve the accuracy by 82.4-95.4%.