Potential and limitation of air pollution mitigation by vegetation and uncertainties of deposition-based evaluations

Metal oxide-based photocatalysts for the efficient degradation of organic pollutants for a sustainable environment: a review

Photocatalytic degradation is a highly efficient technique for eliminating organic pollutants such as antibiotics, organic dyes, toluene, nitrobenzene, cyclohexane, and refinery oil from the environment. The effects of operating conditions, concentrations of contaminants and catalysts, and their impact on the rate of deterioration are the key focuses of this review. This method utilizes light-activated semiconductor catalysts to generate reactive oxygen species that break down contaminants. Modified photocatalysts, such as metal oxides, doped metal oxides, and composite materials, enhance the effectiveness of photocatalytic degradation by improving light absorption and charge separation. Furthermore, operational conditions such as pH, temperature, and light intensity also play a crucial role in enhancing the degradation process. The results indicated that both high pollutant and catalyst concentrations improve the degradation rate up to a threshold, beyond which no significant benefits are observed. The optimal operational conditions were found to significantly enhance photocatalytic efficiency, with a marked increase in degradation rates under ideal settings. Antibiotics and organic dyes generally follow intricate degradation pathways, resulting in the breakdown of these substances into smaller, less detrimental compounds. On the other hand, hydrocarbons such as toluene and cyclohexane, along with nitrobenzene, may necessitate many stages to achieve complete mineralization. Several factors that affect the efficiency of degradation are the characteristics of the photocatalyst, pollutant concentration, light intensity, and the existence of co-catalysts. This approach offers a sustainable alternative for minimizing the amount of organic pollutants present in the environment, contributing to cleaner air and water. Photocatalytic degradation hence holds tremendous potential for remediation of the environment.

Application of Acinetobacter indicus to promote cigarette smoke particulate matter phytoremediation: removal efficiency and plant-microbe interactions

Particulate matter (PM) is one of the most hazardous atmospheric pollutants. Several plant species show high potential to reduce air pollutants and are widely used as green belts to provide clean outdoor spaces for human well-being. However, high PM concentrations cause physiological changes and stress in plants. In this study, 11 species of Thai native perennial plants were exposed to PM generated from tobacco smoke. Wrightia religiosa (Teijsm. & Binn.) Benth. ex Kurz, Bauhinia purpurea DC. ex Walp. and Tectona grandis L.f. reduced PM effectively (which is in the typical range of 43.95 to 52.97%) compared to other plant species. In addition, the responses of perennial plants under PM stress at the proteomic level were also evaluated. Proteomic analysis of these three plant species showed that plants respond negatively to high PM concentrations, such as reducing several photosynthetic-related proteins and increasing plant stress response proteins. To improve PM phytoremediation efficiency and reduce plant stress from PM, perennial plant-microbe interactions were investigated. W. religiosa was inoculated with Acinetobacter indicus PS1, and high biosurfactant-producing strains clearly showed a higher PM removal efficiency than non-inoculated plants (9.48, 9.5 and 12.6% for PM1.0, PM2.5 and PM10, respectively). Inoculating W. religiosa with A. indicus PS1 maintained chlorophyll a and b concentrations. Moreover, the malondialdehyde (MDA) concentration of W. religiosa inoculated with A. indicus PS1 was lower than that of non-inoculated W. religiosa. The leaf wax content (µg/cm2) and biosurfactant (µg/cm2) of W. religiosa inoculated with A. indicus PS1 were also higher than those of non-inoculated W. religiosa. This study clearly showed that inoculating plants with A. indicus PS1 can help plants remediate PM and improve their PM stress response.

Reassessing the role of urban green space in air pollution control

The assumption that vegetation improves air quality is prevalent in scientific, popular, and political discourse. However, experimental and modeling studies show the effect of green space on air pollutant concentrations in urban settings is highly variable and context specific. We revisited the link between vegetation and air quality using satellite-derived changes of urban green space and air pollutant concentrations from 2,615 established monitoring stations over Europe and the United States. Between 2010 and 2019, stations recorded declines in ambient NO2, (particulate matter) PM10, and PM2.5 (average of -3.14% y-1), but not O3 (+0.5% y-1), pointing to the general success of recent policy interventions to restrict anthropogenic emissions. The effect size of total green space on air pollution was weak and highly variable, particularly at the street scale (15 to 60 m radius) where vegetation can restrict ventilation. However, when isolating changes in tree cover, we found a negative association with air pollution at borough to city scales (120 to 16,000 m) particularly for O3 and PM. The effect of green space was smaller than the pollutant deposition and dispersion effects of meteorological drivers including precipitation, humidity, and wind speed. When averaged across spatial scales, a one SD increase in green space resulted in a 0.8% (95% CI: -3.5 to 2%) decline in air pollution. Our findings suggest that while urban greening may improve air quality at the borough-to-city scale, the impact is moderate and may have detrimental street-level effects depending on aerodynamic factors like vegetation type and urban form.

Nature contact and general health: Testing multiple serial mediation pathways with data from adults in 18 countries

The role of neighbourhood nature in promoting good health is increasingly recognised in policy and practice, but consistent evidence for the underlying mechanisms is lacking. Heterogeneity in exposure methods, outcome measures, and population characteristics, little exploration of recreational use or the role of different types of green or blue space, and multiple separate mediation models in previous studies have limited our ability to synthesise findings and draw clear conclusions. We examined multiple pathways linking different types of neighbourhood nature with general health using a harmonised international sample of adults. Using cross-sectional survey data from 18 countries (n = 15,917), we developed a multigroup path model to test theorised pathways, controlling for sociodemographic variables. We tested the possibility that neighbourhood nature (e.g. greenspace, inland bluespace, and coastal bluespace) would be associated with general health through lower air pollution exposure, greater physical activity attainment, more social contact, and higher subjective well-being. However, our central prediction was that associations between different types of neighbourhood nature and general health would largely be serially mediated by recent visit frequency to corresponding environment types, and, subsequently, physical activity, social contact, and subjective well-being associated with these frequencies. Several subsidiary analyses assessed the robustness of the results to alternative model specifications as well as effect modification by sociodemographics. Consistent with this prediction, there was statistical support for eight of nine potential serial mediation pathways via visit frequency which held for a range of alternative model specifications. Effect modification by financial strain, sex, age, and urbanicity altered some associations but did not necessarily support the idea that nature reduced health inequalities. The results demonstrate that across countries, theorised nature-health linkages operate primarily through recreational contact with natural environments. This provides arguments for greater efforts to support use of local green/blue spaces for health promotion and disease prevention.

Future impacts of O3 on respiratory hospital admission in the UK from current emissions policies

Exposure to ambient ozone (O3) O3 is associated with impacts on human health. O3 is a secondary pollutant whose concentrations are determined inter alia by emissions of precursors such as oxides of nitrogen (NOx) and volatile organic compounds (VOCs), and thus future health burdens depend on policies relating to climate and air quality. While emission controls are expected to reduce levels of PM2.5 and NO2 and their associated mortality burdens, for secondary pollutants like O3 the picture is less clear. Detailed assessments are necessary to provide quantitative estimates of future impacts to support decision-makers. We simulate future O3 across the UK using a high spatial resolution atmospheric chemistry model with current UK and European policy projections for 2030, 2040 and 2050, and use UK regional population-weighting and latest recommendations on health impact assessment to quantify respiratory emergency hospital admissions associated with short-term effects of O3. We estimate 60,488 admissions in 2018, increasing by 4.2%, 4.5% and 4.6% by 2030, 2040 and 2050 respectively (assuming a fixed population). Including future population growth, estimated emergency respiratory hospital admissions are 8.3%, 10.3% and 11.7% higher by 2030, 2040 and 2050 respectively. Increasing O3 concentrations in future are driven by reduced nitric oxide (NO) in urban areas due to reduced emissions, with increases in O3 mainly occurring in areas with lowest O3 concentrations currently. Meteorology influences episodes of O3 on a day-to-day basis, although a sensitivity study indicates that annual totals of hospital admissions are only slightly impacted by meteorological year. While reducing emissions results in overall benefits to population health (through reduced mortality due to long-term exposure to PM2.5 and NO2), due to the complex chemistry, as NO emissions reduce there are associated local increases in O3 close to population centres that may increase harms to health.

Impacts of emissions policies on future UK mortality burdens associated with air pollution

Air pollution is the greatest environmental risk to public health. Future air pollution concentrations are primarily determined by precursor emissions, which are driven by environmental policies relating to climate and air pollution. Detailed health impact assessments (HIA) are necessary to provide quantitative estimates of the impacts of future air pollution to support decision-makers developing environmental policy and targets. In this study we use high spatial resolution atmospheric chemistry modelling to simulate future air pollution concentrations across the UK for 2030, 2040 and 2050 based on current UK and European policy projections. We combine UK regional population-weighted concentrations with the latest epidemiological relationships to quantify mortality associated with changes in PM_2.5 and NO₂ air pollution. Our HIA suggests that by 2050, population-weighted exposure to PM_2.5 will reduce by 28% to 36%, and for NO₂ by 35% to 49%, depending on region. The HIA shows that for present day (2018), annual mortality attributable to the effects of long-term exposure to PM_2.5 and NO₂ is in the range 26,287 - 42,442, and that mortality burdens in future will be substantially reduced, being lower by 31%, 35%, and 37% in 2030, 2040 and 2050 respectively (relative to 2018) assuming no population changes. Including population projections (increases in all regions for 30+ years age group) slightly offsets these health benefits, resulting in reductions of 25%, 27%, and 26% in mortality burdens for 2030, 2040, 2050 respectively. Significant reductions in future mortality burdens are estimated and, importantly for public health, the majority of benefits are achieved early on in the future timeline simulated, though further efforts are likely needed to reduce impacts of air pollution to health.

Air pollutant removal by four sidewalk tree species in the largest city in Taiwan

Air pollutants pose risks to human health, especially in densely populated cities. We compared the interception of suspended particles and metal elements by four sidewalk tree species with different leaf surface wettability (based on contact angle), leaf area, and phenology in Taipei, Taiwan. Suspended particles were enriched 2.0-2.5 times in throughfall relative to rainfall due to wash-off of suspended particles deposited on leaf surfaces during rainless periods. The enrichment in throughfall was greater in tree species with larger leaf areas. Despite greater concentrations of suspended particles in rainfall during the low-leaf-area period, enrichment was greater in the high-leaf-area period, indicating that leaf area was a key factor affecting canopy interception of pollutants. Throughfall enrichment of suspended particles positively correlated with water quantity, indicating that air pollutants intercepted by tree canopies were not fully washed off by rainfall. Annually, ∼830 g of suspended particles were intercepted and washed off from one tree canopy, with a crown area of 42 m² . Scaling up, a rough estimate of 72.7 Mg of suspended particles were intercepted annually by the 90,000 sidewalk trees in Taipei City. Copper, chromium, and aluminum were enriched in throughfall compared with rainfall. However, lead was depleted in throughfall, indicating greater interception than wash-off. Based on our results, leaf area and length of foliated period are key characteristics affecting canopy interception of particulate matter and associated metal elements, whereas leaf surface wettability is of secondary importance.

A European-Chinese Exploration: Part 2—Urban Ecosystem Service Patterns, Processes, and Contributions to Environmental Equity under Different Scenarios

Urban expansion and ecological restoration policies can simultaneously affect land-cover changes and further affect ecosystem services (ES). However, it is unclear whether and to what extent the distribution and equity of urban ES are influenced by the stage of urban development and government policies. This study aims to assess the quantity and equity of ES under different scenarios in cites of China and Europe. Firstly, we used the Conversion of Land Use and its Effects at Small regional extent (CLUE-S) model to simulate future land cover under three scenarios: business-as-usual (BAU), a market-liberal scenario (MLS), and an ecological protection scenario (EPS). Then using ecosystem service model approaches and the landscape analysis, the dynamics of green infrastructure (GI) fraction and connectivity, carbon sequestration, and PM2.5 removal were further evaluated. The results show that: (1) over the past 20 years, Chinese cities have experienced dramatic changes in land cover and ES relative to European cities. (2) Two metropolises in China, Shanghai and Beijing have experienced an increase in the fraction and connectivity of GI and ES in the long-term built-up areas between 2010 and 2020. (3) EPS scenarios are not only effective in increasing the quantity of ES but also in improving the equity of ES distribution. The proposed framework as well as the results may provide important guidance for future urban planning and sustainable city development.

The relationship between greenspace and personal exposure to PM2.5 during walking trips in Delhi, India

The presence of urban greenspace may lead to reduced personal exposure to air pollution via several mechanisms, for example, increased dispersion of airborne particulates; however, there is a lack of real-time evidence across different urban contexts. Study participants were 79 adolescents with asthma who lived in Delhi, India and were recruited to the Delhi Air Pollution and Health Effects (DAPHNE) study. Participants were monitored continuously for exposure to PM_2.5 (particulate matter with an aerodynamic diameter of less than 2.5 μm) for 48 h. We isolated normal day-to-day walking journeys (n = 199) from the personal monitoring dataset and assessed the relationship between greenspace and personal PM_2.5 using different spatial scales of the mean Normalised Difference Vegetation Index (NDVI), mean tree cover (TC), and proportion of surrounding green land use (GLU) and parks or forests (PF). The journeys had a mean duration of 12.7 (range 5, 53) min and mean PM_2.5 personal exposure of 133.9 (standard deviation = 114.8) μg/m³. The within-trip analysis showed weak inverse associations between greenspace markers and PM_2.5 concentrations only in the spring/summer/monsoon season, with statistically significant associations for TC at the 25 and 50 m buffers in adjusted models. Between-trip analysis also indicated inverse associations for NDVI and TC, but suggested positive associations for GLU and PF in the spring/summer/monsoon season; no overall patterns of association were evident in the autumn/winter season. Associations between greenspace and personal PM_2.5 during walking trips in Delhi varied across metrics, spatial scales, and season, but were most consistent for TC. These mixed findings may partly relate to journeys being dominated by walking along roads and small effects on PM_2.5 of small pockets of greenspace. Larger areas of greenspace may, however, give rise to observable spatial effects on PM_2.5, which vary by season.

Machine Learning for Determining Interactions between Air Pollutants and Environmental Parameters in Three Cities of Iran

Air pollution, as one of the most significant environmental challenges, has adversely affected the global economy, human health, and ecosystems. Consequently, comprehensive research is being conducted to provide solutions to air quality management. Recently, it has been demonstrated that environmental parameters, including temperature, relative humidity, wind speed, air pressure, and vegetation, interact with air pollutants, such as particulate matter (PM), NO2, SO2, O3, and CO, contributing to frameworks for forecasting air quality. The objective of the present study is to explore these interactions in three Iranian metropolises of Tehran, Tabriz, and Shiraz from 2015 to 2019 and develop a machine learning-based model to predict daily air pollution. Three distinct assessment criteria were used to assess the proposed XGBoost model, including R squared (R2), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE). Preliminary results showed that although air pollutants were significantly associated with meteorological factors and vegetation, the formulated model had low accuracy in predicting (R2PM2.5 = 0.36, R2PM10 = 0.27, R2NO2 = 0.46, R2SO2 = 0.41, R2O3 = 0.52, and R2CO = 0.38). Accordingly, future studies should consider more variables, including emission data from manufactories and traffic, as well as sunlight and wind direction. It is also suggested that strategies be applied to minimize the lack of observational data by considering second-and third-order interactions between parameters, increasing the number of simultaneous air pollution and meteorological monitoring stations, as well as hybrid machine learning models based on proximal and satellite data.

Location, Location, Location: Modelling of Noise Mitigation by Urban Woodland Shows the Benefit of Targeted Tree Planting in Cities

Noise pollution from road traffic is ubiquitous in modern cities and is the second greatest environmental risk to health in Western Europe. Urban woodland can provide substantial noise mitigation if located properly, yet such considerations are often absent from the urban planning process. Current approaches for quantifying this important ecosystem service (ES) do not account adequately for important spatial factors and are unable to identify effectively the best locations to place new woodland for noise mitigation. We present new methods, in which we exploit the concept of least-cost-distance, to map and value the mitigating effect of urban woodland, and to identify optimal locations to place new woodland. Applying these methods, we show that urban woodland currently provides Birmingham City (UK) with over GBP 3.8 million in noise mitigation benefits, annually. We also show that our new ‘opportunity’ mapping methods effectively identify the best locations for new woodland, achieving close to a maximum service with less than a quarter of the additional woodland needed to achieve it. This has important implications for the design and implementation of urban tree planting for noise mitigation, and these methods can be adapted for other ES, allowing consideration of multiple service outcomes.

Evaluation of the anthropogenic black carbon emissions and deposition on Norway spruce and silver birch foliage in the Baltic region

This study investigates potential influence of urban trees on black carbon (BC) removal by Norway spruce and silver birch along with the BC formation, mass concentration in air, and source apportionment. The main sources of BC in urban areas are transport, household and industry. BC concentrations monitored in urban background station in Vilnius (Lithuania) showed that biomass burning was a significant contributor to BC emissions even during warm period of the year. Therefore, BC emission levels were determined for the most common biomass fuels (mixed wood pellets, oak, ash, birch and spruce firewood) and two types of agro-biomass (triticale and rapeseed straw pellets) burned in modern and old heating systems. The highest emissions were obtained for biomass fuels especially birch firewood. BC aerosol particles produced by the condensation mechanism during the combustion processes were found in all samples taken from the leaf surface. The short-term effect of BC exposure on photosynthetic pigments (chlorophyll a and b; and carotenoids) in the foliage of one-year-old Norway spruce and silver birch seedlings was evaluated by the experiment carried out in the phytotron greenhouse. The seedlings showed different short-term responses to BC exposure. All treatments applied in the phytotron greenhouse resulted in lower chlorophyll content in spruce foliage compared to natural conditions but not differed for birch seedlings. However, the exposure of BC particles on the spruce and birch seedlings in the phytotron increased the content of photosynthetic pigments compared to the control seedlings in the phytotron. Overall, urban trees can help improve air quality by reducing BC levels through dry deposition on tree foliage, and needle-like trees are more efficient than broad-leaved trees in capturing BC. Nevertheless, a further study could assess the longer-term effects of BC particles on tree biochemical and chemical reactions.

Using demand mapping to assess the benefits of urban green and blue space in cities from four continents

The benefits of urban green and blue infrastructure (UGI) are widely discussed, but rarely take into account local conditions or contexts. Although assessments increasingly consider the demand for the ecosystem services that UGI provides, they tend to only map the spatial pattern of pressures such as heat, or air pollution, and lack a wider understanding of where the beneficiaries are located and who will benefit most. We assess UGI in five cities from four continents with contrasting climate, socio-political context, and size. For three example services (air pollution removal, heat mitigation, accessible greenspace), we run an assessment that takes into account spatial patterns in the socio-economic demand for ecosystem services and develops metrics that reflect local context, drawing on the principles of vulnerability assessment. Despite similar overall levels of UGI (from 35 to 50% of urban footprint), the amount of service provided differs substantially between cities. Aggregate cooling ranged from 0.44 °C (Leicester) to 0.98 °C (Medellin), while pollution removal ranged from 488 kg PM_2.5/yr (Zomba) to 48,400 kg PM_2.5/yr (Dhaka). Percentage population with access to nearby greenspace ranged from 82% (Dhaka) to 100% (Zomba). The spatial patterns of pressure, of ecosystem service, and of maximum benefit within a city do not necessarily match, and this has implications for planning optimum locations for UGI in cities.