Pollution Variables Research Articles

Spatial variability and hotspot prediction of heavy metal pollution in soil-rice system: based on a new geostatistical method

ABSTRACT The goal of this research was to better understand the spatial characteristics and hot areas of heavy metal concentrations in the soil-rice system, which was critical for implementing heavy metal pollution prevention and control strategies. The amounts of heavy metals in soil-rice were determined using high-precision testing methods such as XRF, ICP-MS, and AFS based on the systematic collection of 285 pairs of soil-rice samples from Wanjiang Economic Zone, China. The Getis-Ord index, Kriging method, and geostatistical method were used to reveal the spatial distribution of heavy metal contamination hot spots in the soil-rice system. The results revealed that the average concentration of heavy metals in soil was Zn>Cr>Cu>Pb>Ni>As>Cd>Hg, whereas it was Zn>Cu>Ni>Cr>Cd>As>Pb>Hg in rice. The average concentrations of heavy metals in the soil were all well below the maximum allowable limit, except for Cd, which was slightly above the allowable limit. Only Cd and Ni concentrations in rice exceeded the maximum permitted limits. In soil, Cd, Hg, Pb, and Zn pollution hotspots were mostly found in the southeast, while Cr and Ni were mostly found in the northeast. An intriguing discovery was that there was a weak relationship between heavy metal concentrations in soil and rice and the spatial pattern of their distribution. The study’s findings offer a scientific foundation for establishing a long-term agricultural development strategy for the soil-rice system in the Wanjiang Economic Zone.

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

Rising surface ozone due to anthropogenic activities and its impact on COVID-19 related deaths in Delhi, India

The rapidity and global spread of the COVID-19 pandemic have left several vital questions in the research community requiring coordinated investigation and unique perspectives to explore the relationship between the spread of disease and air quality. Previous studies have focused mainly on the relation of particulate matter concentration with COVID-19-related mortalities. In contrast, surficial ozone has not been given much attention as surface ozone is a primary air pollutant and directly impacts the respiratory system of humans. Hence, we analyzed the relationship between surface ozone pollution and COVID-19-related mortalities. In this study, we have analyzed the variability of various atmospheric pollutants (particulate matter (PM2.5 and PM10), Nitrogen dioxide (NO2), Carbon monoxide (CO), and Ozone) in the National Capital Region (NCR) of India during 2020–2021 using station data and investigated the relationship of the air-quality parameters with the COVID-19 related deaths. In northern parts of India, the concentration of particulate matter (PM2.5 and PM10), Nitrogen dioxide (NO2), Carbon monoxide (CO), and Ozone remain high during the pre- and post-monsoon seasons due to dust loading and crop residue burning (after winter wheat in April & summer rice in November). The westerly wind brings the polluted airmass from western and northwestern parts to Delhi and National Capital Region during April–June and October–November, and meteorological conditions help raise the concentration of these pollutants. Due to long solar hours and high CO concentrations, the ozone concentration is higher from April to June and September. While comparing major air quality parameters with COVID-19-related deaths, we found a good relationship between surface ozone and COVID-19 mortality in Delhi. We also observed a time lag relationship between ozone concentration and mortality in Delhi, so the exposure to Ozone in a large population of Delhi may have augmented the rise of COVID-19-related deaths. The analysis suggested that ozone has a significant relationship with COVID-19 related mortality in Delhi in comparison to other parameters.

Spatiotemporal variability and measurement uncertainty in atmospheric polycyclic aromatic hydrocarbons (PAHs): A quantitative variance component analysis

A comprehensive understanding of the spatial and temporal variability in air pollution facilitates air monitoring designs, exposure assessment, and environmental epidemiology research. Polycyclic aromatic hydrocarbons (PAHs) are ubiquitous in the air but are challenging to measure due to their low concentrations. To date, no study has been specifically designed to quantify the spatial and temporal aspects of variability in ambient PAH concentrations. This study aims to decompose the variability in PAH concentrations measured in the ambient air in a metropolitan area into different components. Air samples were collected using high-volume samplers at 19 stations in the Memphis Tri-state Area across Tennessee, Arkansas, and Mississippi, USA, every 12 days from March 2018 to May 2019. Samples were collected on polyurethane forms and quartz fiber filters and analyzed using gas chromatography/mass spectrometry (GC/MS) for 32 PAHs. Variance components of the total variance were computed using mixed models for individual PAHs and sum concentrations of the 16 priority PAHs (Σ16PAHs). For Σ16PAHs, spatial and temporal variability represented 16% and 84% of the total variance. The total variance could be further apportioned to between-urbanicity (0.5%), between-site (5.5%), between-season (41%), and within-season (53%) variances. Sampling and analytical uncertainties represented 39% and 0.01% of the total variance, when monitoring uncertainties were considered. Concentrations of individual PAHs followed similar variance structures. These findings confirmed the validity of centralized monitoring stations to represent a large area, suggested the need for repeated samples across seasons and at multiple time points within a season, and warranted improving quality control for field sample collections.

Associations of COVID-19 Hospitalizations, ICU Admissions, and Mortality with Black and White Race and Their Mediation by Air Pollution and Other Risk Factors in the Louisiana Industrial Corridor, March 2020-August 2021.

Louisiana ranks among the bottom five states for air pollution and mortality. Our objective was to investigate associations between race and Coronavirus Disease 2019 (COVID-19) hospitalizations, intensive care unit (ICU) admissions, and mortality over time and determine which air pollutants and other characteristics may mediate COVID-19-associated outcomes. In our cross-sectional study, we analyzed hospitalizations, ICU admissions, and mortality among positive SARS-CoV-2 cases within a healthcare system around the Louisiana Industrial Corridor over four waves of the pandemic from 1 March 2020 to 31 August 2021. Associations between race and each outcome were tested, and multiple mediation analysis was performed to test if other demographic, socioeconomic, or air pollution variables mediate the race-outcome relationships after adjusting for all available confounders. Race was associated with each outcome over the study duration and during most waves. Early in the pandemic, hospitalization, ICU admission, and mortality rates were greater among Black patients, but as the pandemic progressed, these rates became greater in White patients. However, Black patients were disproportionately represented in these measures. Our findings imply that air pollution might contribute to the disproportionate share of COVID-19 hospitalizations and mortality among Black residents in Louisiana.

Air Pollution and Its Potential Consequences for Tourism and Career Development from Students' Perspective: A Case Study of the Gdańsk Agglomeration in Poland.

The purpose of this study is to present the state and variability of air pollution and its potential consequences for the intensity of tourism traffic based on the example of the Gdańsk agglomeration as a very popular tourist destination of Northern Poland. Specifically, the study seeks to answer the question how a future, qualified tourism workforce, such as tourism and hospitality (T&H) students from higher educational institutions (HEIs) located in the investigated area, perceive the problem of air pollution and whether their perception may have a potential impact on their attitudes and career aspirations towards working in the T&H industry after graduation. In this study, both a desk-research method and a questionnaire were used. The main results reveal that although the intensified tourist traffic does not coincide with high concentrations of pollutants and a poor quality of air, it cannot be clearly stated that tourists choose a destination being guided by the condition of ambient air pollution. The findings also show that T&H students are strongly aware of the air pollution problems and its negative consequences for the perceived attractiveness of a tourist destination and its labour market. To the best of the authors' knowledge, this study is among the first to analyse the relationships between air pollution and students' perceptions of its consequences for tourism and for career development in the tourism industry, which is highly dependent on the environmental quality.

Pollutant concentrations and exposure variability in four urban microenvironments of London

We compared various pollutant concentrations (PM1, PM2.5, PM10, PNC, BC) at four different urban microenvironments (MEs) in London (Indoor, IN; Traffic Intersection, TI; Park, PK; and Street Canyon, SC). The physico-chemical characteristics of particles were analysed, and the respiratory deposition doses (RDD) were estimated. Field measurements were conducted over a period of 121 days. The mean PM2.5 (PNC) concentrations were found to be 9.47 ± 7.05 (16366 ± 11815), 8.09 ± 4.57 (10951 ± 6445), 5.11 ± 2.96 (7717 ± 4576), 3.88 ± 3.06 (5672 ± 2934) μg m−3 (# cm−3) at TI, SC, PK and IN, respectively. PM2.5, PM10 and PNC exhibited a trend of TI > SC > PK > IN; higher concentrations for PM1 and BC were observed at IN than PK due to the emissions from printers, producing a trend of TI > SC > IN > PK. We observed 12%–30% higher fine PM concentrations at TI and SC sites during morning peak (07:00–09:30) than the evening peak hours (16:00–19:00); while IN showed a smaller variation in fine PM concentrations compared with outdoor TI, PK and SC sites owing to their prevalence in the IN for a longer time. Fine and ultrafine PM containing potentially toxic trace transition metals including Fe, Ti, Cr, Mn, Al and Mg were detected by high resolution electron microscopy at all sites. There was a similar relative abundance of different elements at the TI, IN and PK sites, which suggests a transport of PM between MEs. RDD for PM1 was highest (2.45 ± 2.27 μg h−1) at TI for females during running; PM2.5 and PM10 were highest at SC (11.23 ± 6.34 and 37.17 ± 20.82 μg h−1, respectively). The results show that the RDD variation between MEs does not follow the PM concentration trend. RDD at PK was found to be 39%–53% lower than TI and SC during running for all the PM fractions. Overall, the study findings show the air quality variation at different MEs and reveals the exposure inequalities around the city, which enable the management of personal exposure by selecting appropriate MEs for different activities.

Biomonitoring of polycyclic aromatic hydrocarbons, heavy metals, and plasticizers residues: role of bees and honey as bioindicators of environmental contamination.

Polycyclic aromatic hydrocarbons (PAHs), heavy metals, and plasticizer residues are continuously released into the environment. The use of living organisms, such as Apis mellifera L. and honey, is advantageous as bioindicator of the environmental health status, instead of traditional monitoring methods, showing the ability to record spatial and temporal pollutant variations. The PAHs and heavy metal presence were determined in two sampling years (2017 and 2018) in five different locations in the Molise region (Italy), characterized by different pollution levels. During 2017, most PAHs in all samples were lower than limit of detection (LOD), while in 2018, their mean concentration in bee and honey samples was of 3μgkg-1 and 35μgkg-1, respectively. For heavy metals, lower values were detected in 2017 (Be, Cd, and V below LOD), while in 2018, the mean concentrations were higher, 138μgkg-1 and 69μgkg-1, in bees and honey, respectively. Honey has been used as indicator of the presence of phthalate esters and bisphenol A in the environment. The satisfactory results confirmed that both bees and honey are an important tool for environmental monitoring. The chemometric analysis highlighted the differences in terms of pollutant concentration and variability in the different areas, validating the suitability of these matrices as bioindicators.

Meteorological, PM2.5 and PM10 factors on SARS-COV-2 transmission: The case of southern regions in Chile

There are several determinants of a population's health, including meteorological factors and air pollution. For example, it is well known that low temperatures and air pollution increase mortality rates in infant and elderly populations. With the emergence of SARS-COV-2, it is important to understand what factors contribute to its mitigation and control. There is some research in this area which shows scientific evidence on the virus's behavior in the face of these variables. This research aims to quantify the impact of climatic factors and environmental pollution on SARS-COV-2 specifically the effect on the number of new infections in different areas of Chile. At the local level, historical information available from the Department of Statistics and Health Information, the Chilean National Air Quality Information System, the Chilean Meteorological Directorate, and other databases will allow the generation of panel data suitable for the analysis. The results show the significant effect of pollution and climate variables measured in lags and will allow us to explain the behavior of the pandemic by identifying the relevant factors affecting health, using heteroskedastic models, which in turn will serve as a contribution to the generation of more effective and timely public policies for the control of the pandemic.

Evaluation of low-cost gas sensors to quantify intra-urban variability of atmospheric pollutants

Low-cost electrochemical air quality sensors can provide deep insights into the intra-urban variability of different air pollutants after proper calibration using field co-location with regulatory Air Quality Monitoring stations.

Loss Characteristics of Non-point Pollutant from a Mixed Agriculture Watershed

In order to explore the temporal and spatial variability of non-point source pollution in an agriculture watershed, the water quality and quantity characteristics of five flood events at Shuangqiao station in Siheshui watershed (south China) were monitored. Five pollutants, including BOD5, TSS, TP, TN, and NH3-N, were measured and analyzed. The characteristics of pollutant concentrations and load-transport rates were investigated. It is shown that the pollutant concentrations in the flood events are remarkably larger than those in the non-flood events. The temporal sequences of peak values between pollutant parameters and runoff flows are explored. It indicates that the time interval would be short if the high-intensity rainfall occurred in the initial period of a flood event. Most of the peak values of pollutant concentrations preceded the flow peaks. The correlation coefficients between TSS and other pollutant concentrations are analyzed. It is shown that there is a significant correlation between the concentrations of TSS and TP in the flood events.

Indigenous Knowledge of seasons delivers a new way of considering annual cycles in atmospheric dispersion of pollutants

Poor air quality is recognised as the most important environmental health issue of our time. Meteorological variables like temperature and wind speed can strongly influence air quality and these variables often show clear annual cycles. It is therefore common to analyse atmospheric pollutants within a seasonal framework. However, the commonly used seasons in Australia do not align well with all of the most important annual weather patterns that influence air quality in the Sydney Basin. We used Indigenous perspectives on ‘seasons’ as identified by the co-authors and combined these with statistical analysis of the local climatology. This enabled us to create a set of locally informed ‘quasi-seasons’ that we named IKALC-seasons (Indigenous Knowledge Applied to Local Climatology). Engaging with the IKALC-seasons improved our understanding of temporal variability of air pollution in western Sydney, mainly due to a better identification of the time of year when cold, still weather conditions result in higher levels of fine particulate pollution, carbon monoxide and nitrogen oxides. Although the IKALC seasons identified in this study are intrinsically local in nature, the methodology developed has broadscale application. This approach can be used to identify the times of year when micrometeorological conditions are most likely to drive poor air quality thereby helping to inform effective decision-making about emission controls.

Estimating Near-Surface Concentrations of Major Air Pollutants From Space: A Universal Estimation Framework LAPSO

Like many other countries, China is still facing severe air pollution issues after extensive efforts. The difficulties in deriving near-surface concentrations from satellite measurements restrict the application of remote sensing of large-scale surface air quality. Aiming at providing daily accurate near-surface ail pollution estimates (PM <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2.5</sub> , PM <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">10</sub> , O <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sub> , NO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> , SO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> , and CO), we propose a robust estimation framework called Learning Air Pollutants from Satellite Observations (LAPSO). The principle of LAPSO is to derive a non-linear relationship between surface pollutant concentrations of interest and satellite observations with the aid of meteorological reanalyses based on deep learning techniques. The LAPSO framework is superior to other algorithms due to its robust retrieval performance, independence from chemical transport models, lower hardware requirements as well as a user-friendly interface. The retrieval results of LAPSO were in good agreement with ground-level measurements according to extensive cross-validation at 1628 sites (R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> > 0.8 in polluted areas and uncertainty ≪ 5 μg/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> for most pollutants) in China. The framework also showed a strong capability to capture the temporal variability of different air pollutants. By comparing with the estimation results from different satellite platforms, TROPOMI onboard the Sentinel-5P demonstrated marginally better performance for estimating PM <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2.5</sub> . Although the selection of satellite observations did not significantly affect the results of O <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sub> estimation, the number and spatial sampling density of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in-situ</i> sites imposed large impacts on the O <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sub> estimation performance. The success of LAPSO for estimating near-surface concentrations from satellite remote sensing at an enhanced spatiotemporal resolution is expected to serve the continuous and dynamical monitoring of regional and global air pollution.

Modelling the Impact of Road Dust on Air Pollution: A Sustainable System Dynamics Approach

Road dust contributes significantly to air pollution by releasing fine particulate matter (PM) and other pollutants into the air, which can cause respiratory and cardiovascular problems and premature death. This dust is generated through the wear and tear of vehicle tires and road surfaces, as well as the accumulation of dirt and debris on the road, primarily from construction activities and cargo trucks carrying building materials. Wind, weather conditions, and vehicle movement play crucial roles in the distribution and concentration of these particles in the air. To address this issue, this paper focuses on identifying various variables that are connected to road dust operations and their interrelationships with air pollution variables, representing the dynamic pattern of the entire system. The paper proposes the establishment of a sustainable causal-loop model using system dynamics (SD) modeling in Vensim, connecting feedback mechanisms to effectively control the road dust concentration. Additionally, the paper suggests different policy interventions applied to the whole system to achieve optimized results. In the future, this research aims to convert and simulate the causal-loop model to a stock-flow model and compare the effectiveness of different policy interventions to further reduce road dust contributing to air pollution.

Associations between meteorological factors, air pollution and Legionnaires’ disease in New Zealand: Time series analysis

BackgroundPrior studies have shown that meteorological factors may be associated with increases in legionellosis (Legionnaire's disease, (LD)), caused by Legionella, a globally ubiquitous bacterium found naturally in aquatic habitats, soils, and compost. The aim of this retrospective time series analysis was to examine the association between meteorological factors and air pollution parameters and the incidence of sporadic, community-acquired, laboratory confirmed LD. MethodsDaily cases of community-acquired legionellosis, meteorological and air pollution data from two urban areas, Auckland (North Island) and Christchurch (South Island) were collected from January 1, 1997 until December 31, 2020. Using Quasi-Poisson regression, associations between symptom onset and meteorological and air pollution variables were investigated using an interrupted time series analysis. ResultsThe two cities had different meteorological conditions and LD epidemiology and seasonal patterns of Legionella spp. LD incidence rates (per 100,000 population) were higher in Christchurch than Auckland for L.pneumophila (25.8 vs 10.8) and L.longbeachae (78.2 vs 4.9). Seasonal patterns were detected in Christchurch with a higher risk of LD during spring and summer (RR: 1.87, 95% CI: 1.42, 2.49) compared to autumn and winter months. In Auckland, the level of particulate matter 9–10 days prior to the onset date was positively associated with LD occurrence (RR: 1.02, 95% CI: 1.00, 1.04) compared to Christchurch, where Tmax recorded one day prior the onset (RR: 1.03, 95% CI: 1.00, 1.07) and sulphur dioxide 6 days prior to the onset date (RR: 1.27, 95% CI: 1.10, 1.45) were positively associated with LD occurrence. Atmospheric pressure 12 days prior (RR: 0.95, 95% CI: 0.90, 1.00) and wind speed 13 days prior (RR: 0.94, 95% CI: 0.89, 0.99) to the onset date were negatively associated with LD risk. In both cities, no association was detected between the level of precipitation and LD risk. ConclusionsMeteorological factors and air pollutants were associated with the risk of LD. However, different seasonal patterns and prevalent LD species seem to have distinct patterns of association between the two categories of exposures. These findings suggest the importance of considering meteorological and air quality conditions in conjunction with the source of exposure and the LD species involved. They also imply potential climate change impacts on LD and benefits from reducing air pollution, though findings need to be replicated in other geographical regions.

Seasonal influences on microbiological quality of aquatic ecosystems

The wide distribution of bacteria and their highly dynamic activities have a great impact on the aquatic ecosystem. Surface water bodies constitute an important drinking water source that approximatively cover half of the worldwide need. Considering the main health threats, the microbial profile represents the most important indicator on the biology quality of water environments. One of the most important criterion is fecal indicators and in consequence the aim of this study was to monitor the seasonal fecal pollution variability in the surface water of Arges and Dambovita River during 2021. These rivers are part of the Arges-Vedea basin that cover 9% of Romania’s surface and they are located in the proximity of the Bucharest, the capital city of Romania.

Angiosperms distribution under the influence of microclimatic factors across a polluted ecosystem

Plant species represent the hierarchical expression of vegetation as it is affected by different ecological, environmental factors and pollution variables. This study assesses the impact of a different hidden treasure or factors on angiosperm plants in the marble waste polluted environment. Quadrat quantitative ecological techniques were adapted for sampling of vegetation. Size of the quadrats was kept 1 × 1m, 5 × 5m and 10 × 10m for herbs, shrubs and tree species, respectively. Soil physicochemical properties were carried out using standard protocols. All the collected data were put in MS Excel for analysis in PCORD, CANOCO, STATA and SPSS software's. The degrees of relationship among variables and with abundant plants were measured using Pearson correlation and regression analysis. The structural relationship between environmental variables and abundant angiosperms was determined through Structural Equation Modeling to examine the admired dignity of diverse environmental factors. A total of one hundred two plant species belong to ninety-five genera and forty-eight families were reported from a marble waste polluted environment. Cluster Analysis via Jaccard distance measurement and ward linkage methods gave rise to three plant communities i.e. (i) Populus alba-Arundo donax-Erigeron canadensis plants community, (ii) Morus alba- Rosa webbiana- Centaurium tenuiflorum plants community and (iii) Dalbergia sissoo- Dodonaea viscosa- Panicum antidotale plants community. Among all the measured environmental variables, the dominant plants of the first plant community comprehend positive and significant relationships with soil pH, potassium and negative plus significant relationship with electrical conductivity and organic matter. The abundant plants of 2nd plant community were under the influence of higher potassium concentration and lower soil pH as compared to other communities. Third community exhibits a negative and significant relationship with soil pH in conjunction with higher electrical conductivity in the region. The aforementioned techniques could be used to find out the relationship between plant species distribution and microclimatic variables.

Seasonal Variability of Air Pollutants and Their Relationships to Meteorological Parameters in an Urban Environment

Air quality in urban areas is deteriorating over time with the increased pollutant distribution levels mainly caused due to anthropogenic activities. In addition, these pollutant distribution levels may relate to changing meteorological conditions. However, the relationships were not researched in-depth in the context of Sri Lanka, a country with a significant impact on climate change. The main objective of this study was to provide a broader perspective on the seasonal variation of tiny particles in air (PM2.5 and PM10), nitrogen dioxide (NO2), carbon monoxide (CO), ozone (O3), and sulfur dioxide (SO2) in two urban cities (Colombo and Kandy) in Sri Lanka over 3 years period (2018–2021) and the possible relationships between air pollution and meteorological variables. Results show that all the aforementioned pollutants except O3 consistently depict two peaks during the day, one in the morning (∼07:00–09:00 local time) and the other in the evening (∼18:00–20:00 local time). These peaks coincided with the traffic jams observed in both cities. The results further revealed that the concentration of all pollutants has significant seasonal variations. Compared to two monsoon seasons, the highest daily average PM2.5 (31.2 μg/m3), PM10 (49.5 μg/m3), NO2 (18.9 ppb), CO (717.5 ppb), O3 (18.5 ppb), and SO2 (9.4 ppb) concentrations in Colombo are recorded during northeast monsoon (NEM) seasons while contrast pattern is observed in Kandy. In addition, it was found that wind speed with its direction is the most influencing factor for the pollutant concentration except for SO2 and O3 in two cities, and this is irrespective of the season. This study’s findings contribute to understanding the seasonality of ambient air quality and the relationship between meteorological factors and air pollutants. These findings ultimately lead to designing and implementing season-specific control strategies to achieve air pollution reduction at a regional scale.

Assessment and Spatiotemporal Variability of Heavy Metals Pollution in Water and Sediments of a Coastal Landscape at the Nile Delta

This study assessed the spatiotemporal variability and pollution grades of heavy metals in water and sediments of Bahr El-Baqar drain, Eastern Nile Delta, Egypt, by integration of geochemical analysis, metal pollution indices, correlation, and multivariate statistical analyses. Twenty samples of water and sediments were collected during 2018 and analyzed for heavy metal concentrations using ICP-OES. Heavy metal contents in the water samples followed the order: Fe &gt; Zn &gt; Al &gt; Pb &gt; Mn &gt; Cu &gt; Ni. The drain sediments were highly contaminated with heavy metals that followed the order: Fe &gt; Al &gt; Mn &gt; V &gt; Zn &gt; Cu &gt; Cr &gt; Ba &gt; Ni &gt; Pb &gt; As. Spatiotemporally, most metals in the drain sediments showed a decreasing trend from upstream (south) to downstream sites (north). Results of principal component analysis (PCA) supported those from the Pearson correlation between investigated heavy metals. In water, Mn, Ni, Pb, Zn, Cu, and Fe showed highly significant correlations. In sediments, Ba, Ni, Zn, Fe, Al, Mn, and V showed strong positive correlations indicating that these metals were derived from similar anthropogenic sources. The calculated metal pollution indices: enrichment factor (EF), contamination factor (CF), pollution load index (PLI), degree of contamination (DC), and index of geo-accumulation (Igeo) indicated high loadings of heavy metals in the drain sediments. EFs revealed low, moderate to significant enrichment, whereas CFs showed low, moderate, and considerable contamination. PLI indicated low, baseline, and progressive contamination, while DC indicated low, moderate, and considerable degree of contamination. Igeo of all investigated metals (except for As; class 1) indicated extremely contaminated sediments (class 7).

A parent-school initiative to assess and predict air quality around a heavily trafficked school

Many primary schools in the UK are situated in close proximity to heavily-trafficked roads, yet long-term air pollution monitoring around such schools to establish factors affecting the variability of exposure is limited. We co-designed a study to monitor particulate matter in different size fractions (PM1, PM2.5, PM10), gaseous pollutants (NO2, O3 and CO) and meteorological parameters (ambient temperature, relative humidity) over a period of one year. The period included phases of national COVID-19 lockdown and its subsequent easing and removal. Statistical analysis was used to assess the diurnal patterns, pollution hotspots and underlying factors driving changes. A pollution episode was observed early in January 2021, owing to new year celebration fireworks, when the daily average PM2.5 was around three-times the World Health Organisation limit. PM2.5 and NO2 exceeded the threshold limits on 15 and 10 days, respectively, as the lockdown eased and the school reopened, despite the predominant wind direction often being away from the school towards the roads. The peak concentration levels for all pollutants occurred during morning drop-off hours; however, some weekends showed higher or comparable concentrations to those during weekdays. The strong disproportional Pearson correlation between CO and temperature demonstrated the possible contribution of local sources through biomass burning. The impact of lifting restrictions after removing the weather impact showed that the average pollution levels were low in the beginning and increased by up to 22.7 % and 4.2 % for PM2.5 and NO2, respectively, with complete easing of lockdown. The Prophet algorithm was implemented to develop a prediction model using an NO2 dataset that performed moderately (R2, 0.48) for a new monthly dataset. This study was able to build a local air pollution database at a school gate, which enabled an understanding of the air pollution variability across the year and allowed evidence-based mitigation strategies to be devised.