Spatiotemporal variability of nitrogen dioxide (NO2) pollution in Manchester (UK) city centre (2017-2018) using a fine spatial scale single-NOx diffusion tube network

Monthly average air pollution models using geographically weighted regression in Europe from 2000 to 2019

Detailed spatial models of monthly air pollution levels at a very fine spatial resolution (25 m) can help facilitate studies to explore critical time-windows of exposure at intermediate term. Seasonal changes in air pollution may affect both levels and spatial patterns of air pollution across Europe. We built Europe-wide land-use regression (LUR) models to estimate monthly concentrations of regulated air pollutants (NO2, O3, PM10 and PM2.5) between 2000 and 2019. Monthly average concentrations were collected from routine monitoring stations. Including both monthly-fixed and -varying spatial variables, we used supervised linear regression (SLR) to select predictors and geographically weighted regression (GWR) to estimate spatially-varying regression coefficients for each month. Model performance was assessed with 5-fold cross-validation (CV). We also compared the performance of the monthly LUR models with monthly adjusted concentrations. Results revealed significant monthly variations in both estimates and model structure, particularly for O3, PM10, and PM2.5. The 5-fold CV showed generally good performance of the monthly GWR models across months and years (5-fold CV R2: 0.31-0.66 for NO2, 0.4-0.79 for O3, 0.4-0.78 for PM10, 0.46-0.87 for PM2.5). Monthly GWR models slightly outperformed monthly-adjusted models. Correlations between monthly GWR model were generally moderate to high (Pearson correlation >0.6). In conclusion, we are the first to develop robust monthly LUR models for air pollution in Europe. These monthly LUR models, at a 25 m spatial resolution, enhance epidemiologists to better characterize Europe-wide intermediate-term health effects related to air pollution, facilitating investigations into critical exposure time windows in birth cohort studies.

Lichen biomonitoring to assess spatial variability, potential sources and human health risks of polycyclic aromatic hydrocarbons (PAHs) and airborne metal concentrations in Manchester (UK)

Airborne metals and organic pollutants are linked to severe human health impacts, i.e. affecting the nervous system and being associated with cancer. Airborne metals and polycyclic aromatic hydrocarbons (PAHs) in urban environments are derived from diverse sources, including combustion and industrial and vehicular emissions, posing a threat to air quality and subsequently human health. A lichen biomonitoring approach was used to assess spatial variability of airborne metals and PAHs, identify potential pollution sources and assess human health risks across the City of Manchester (UK). Metal concentrations recorded in lichen samples were highest within the city centre area and along the major road network, and lichen PAH profiles were dominated by 4-ring PAHs (189.82 ng g-1 in Xanthoria parietina), with 5- and 6-ring PAHs also contributing to the overall PAH profile. Cluster analysis and pollution index factor (PIF) calculations for lichen-derived metal concentrations suggested deteriorated air quality being primarily linked to vehicular emissions. Comparably, PAH diagnostic ratios identified vehicular sources as a primary cause of PAH pollution across Manchester. However, local more complex sources (e.g. industrial emissions) were further identified. Human health risk assessment found a "moderate" risk for adults and children by airborne potential harmful element (PHEs) concentrations, whereas PAH exposure in Manchester is potentially linked to 1455 (ILCR = 1.45 × 10-3) cancer cases (in 1,000,000). Findings of this study indicate that an easy-to-use lichen biomonitoring approach can aid to identify hotspots of impaired air quality and potential human health impacts by airborne metals and PAHs across an urban environment, particularly at locations that are not continuously covered by (non-)automated air quality measurement programmes.

Satellite or ground-based measurements for air pollutants (PM2.5, PM10, SO2, NO2, O3) data and their health hazards: which is most accurate and why?

Air pollution is growing at alarming rates on regional and global levels, with significant consequences for human health, ecosystems, and change in climatic conditions. The present 12 weeks (4 October 2021, to 26 December 2021) study revealed the different ambient air quality parameters, i.e., PM2.5, PM10, SO2, NO2, and O3 over four different sampling stations of Delhi-NCR region (Dwarka, Knowledge park III, Sector 125, and Vivek Vihar), India, by using satellite remote sensing data (MERRA-2, OMI, and Aura Satellite) and different ground-based instruments. The ground-based observation revealed the mean concentration of PM2.5 in Dwarka, Knowledge park III, Sector 125, and Vivek Vihar as 279 µg m-3, 274 µg m-3, 294 µg m-3, and 365 µg m-3, respectively. The ground-based instrumental concentration of PM2.5 was greater than that of satellite observations, while as for SO2 and NO2, the mean concentration of satellite-based monitoring was higher as compared to other contaminants. Negative and positive correlations were observed among particulate matter, trace gases, and various meteorological parameters. The wind direction proved to be one of the prominent parameter to alter the variation of these pollutants. The current study provides a perception into an observable behavior of particulate matter, trace gases, their variation with meteorological parameters, their health hazards, and the gap between the measurements of satellite remote sensing and ground-based measurements.

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 NO_x and NH_x 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.

Distinguishing atmospheric nitrogen compounds (nitrate and ammonium) in lichen biomonitoring studies

Nitrogen speciation, i.e. distinguishing nitrate (NO₃^-) and ammonium (NH₄⁺), is commonly undertaken in soil studies, but has not been conducted extensively for lichens. Lichen total nitrogen contents (N wt%) reflect airborne atmospheric nitrogen loadings, originating from anthropogenic sources (e.g. vehicular and agricultural/livestock emissions). Albeit nitrogen being an essential lichen nutrient, nitrogen compound (i.e. NO₃^- and NH₄⁺) concentrations in the atmosphere can have deleterious effects on lichens. Moreover, N wt% do not provide information on individual nitrogen compounds, i.e. NO₃^- and NH₄⁺ which are major constituents of atmospheric particulate matter (e.g. PM₁₀ and PM_2.5). This study presents a novel method to separate and quantify NO₃^- and NH₄⁺ extracted from lichen material. An optimal approach was identified by testing different strengths and volumes of potassium chloride (KCl) solutions and variable extraction times, i.e. the use of 3% KCl for 6 hours can achieve a same-day extraction and subsequent ion chromatography (IC) analysis for reproducible lichen nitrate and ammonium concentration determinations. Application of the method was undertaken by comparing urban and rural Xanthoria parietina samples to investigate the relative importance of the two nitrogen compounds in contrasting environments. Findings presented showed that lichen nitrogen compound concentrations varied in rural and urban X. parietina samples, suggesting different atmospheric nitrogen loadings from potentially different sources (e.g. agricultural and traffic) and varied deposition patterns (e.g. urban layout impacts). Despite potential impacts of nitrogen compounds on lichen metabolism, the approach presented here can be used for quantification of two different nitrogen compounds in lichen biomonitoring studies that will provide specific information on spatial and temporal variability of airborne NO₃^- and NH₄⁺ concentrations that act as precursors of particulate matter, affecting air quality and subsequently human health.