Particulate matter concentrations and fluxes within an urban park in Naples

Evaluating the impact of evolving green and grey urban infrastructure on local particulate pollution around city square parks

The relationship between green and grey urban infrastructure, local meteorological conditions, and traffic-related air pollution is complex and dynamic. This case study examined the effect of evolving morphologies around a city square park in Dublin and explores the twin impacts of local urban development (grey) and maturing parks (green) on particulate matter (PM) pollution. A fixed air quality monitoring campaign and computational fluid dynamic modelling (ENVI-met) were used to assess current (baseline) and future scenarios. The baseline results presented the distribution of PM in the study area, with bimodal (PM2.5) and unimodal (PM10) diurnal profiles. The optimal vegetation height for air quality within the park also differed by wind direction with 21 m vegetation optimal for parallel winds (10.45% reduction) and 7 m vegetation optimal for perpendicular winds (30.36% reduction). Increased building heights led to higher PM2.5 concentrations on both footpaths ranging from 25.3 to 37.0% under perpendicular winds, whilst increasing the height of leeward buildings increased PM2.5 concentrations by up to 30.9% under parallel winds. The findings from this study provide evidence of the importance of more in-depth analysis of green and grey urban infrastructure in the urban planning decision-making process to avoid deteriorating air quality conditions around city square parks.

Understanding the role of biodiversity in the climate, food, water, energy, transport and health nexus in Europe

Biodiversity underpins the functioning of ecosystems and the diverse benefits that nature provides to people, yet is being lost at an unprecedented rate. To halt or reverse biodiversity loss, it is critical to understand the complex interdependencies between biodiversity and key drivers and sectors to inform the development of holistic policies and actions. We conducted a literature review on the interlinkages between biodiversity and climate change, food, water, energy, transport and health ("the biodiversity nexus"). Evidence extracted from 194 peer-reviewed articles was analysed to assess how biodiversity is being influenced by and is influencing the other nexus elements. Out of the 354 interlinkages between biodiversity and the other nexus elements, 53 % were negative, 29 % were positive and 18 % contained both positive and negative influences. The majority of studies provide evidence of the negative influence of other nexus elements on biodiversity, highlighting the substantial damage being inflicted on nature from human activities. The main types of negative impacts were land or water use/change, land or water degradation, climate change, and direct species fatalities through collisions with infrastructure. Alternatively, evidence of biodiversity having a negative influence on the other nexus elements was limited to the effects of invasive alien species and vector-borne diseases. Furthermore, a range of studies provided evidence of how biodiversity and the other nexus elements can have positive influences on each other through practices that promote co-benefits. These included biodiversity-friendly management in relevant sectors, protection and restoration of ecosystems and species that provide essential ecosystem services, green and blue infrastructure including nature-based solutions, and sustainable and healthy diets that mitigate climate change. The review highlighted the complexity and context-dependency of interlinkages within the biodiversity nexus, but clearly demonstrates the importance of biodiversity in underpinning resilient ecosystems and human well-being in ensuring a sustainable future for people and the planet.

Residential green and blue spaces with nonalcoholic fatty liver disease incidence: Mediating effect of air pollutants

BACKGROUND

This study aimed to investigate the relationship of residential green and blue spaces with incident nonalcoholic fatty liver disease (NAFLD), and explore the potential mediation effects of air pollutants and modification effect of genetic susceptibility.

METHODS

411,200 UK Biobank participants without prior liver diseases were included. Land use data were used to estimate residential green and blue spaces (land coverage percentage) at 300 m and 1000 m buffer. The study outcome was incident NAFLD, ascertained through linkage to hospital admissions and death registry records.

RESULTS

5198 NAFLD cases were documented after a median follow-up of 12.5 years. Green and blue spaces were inversely associated with the hazard of NAFLD: per standard deviation (SD) increment of green space coverage at 300 m (SD: 14.5 %; HR, 0.88, 95 %CI, 0.86-0.91) and 1000 m (SD: 14.1 %; HR, 0.88, 95 %CI, 0.86-0.91) buffer, and blue space coverage at 300 m (SD: 1.0 %; HR,0.95, 95 %CI, 0.93-0.98) and 1000 m (SD: 1.2 %; HR,0.96, 95 %CI, 0.93-0.99) buffer were related with a 4-12 % reduction of NAFLD incidence. The beneficial effects of approximately 25-52 % of green space exposure and about 5-35 % of blue space exposure on NAFLD incidence were mediated by the reduction of PM2.5, NO2 and NOx (All Pindirect effect <0.05). Moreover, genetic susceptibility of NAFLD did not modify the relationship of green and blue spaces with NAFLD incidence.

CONCLUSION

Residential green and blue spaces were inversely related to NAFLD incidence. These results suggest that green and blue spaces are modifiable factors that may help prevent NAFLD, and therefore, can be considered as a novel environmental strategy to promote liver health at the community level, rather than only at the individual level.

A systematic literature review of deep learning neural network for time series air quality forecasting

Rapid progress of industrial development, urbanization and traffic has caused air quality reduction that negatively affects human health and environmental sustainability, especially among developed countries. Numerous studies on the development of air quality forecasting model using machine learning have been conducted to control air pollution. As such, there are significant numbers of reviews on the application of machine learning in air quality forecasting. Shallow architectures of machine learning exhibit several limitations and yield lower forecasting accuracy than deep learning architecture. Deep learning is a new technology in computational intelligence; thus, its application in air quality forecasting is still limited. This study aims to investigate the deep learning applications in time series air quality forecasting. Owing to this, literature search is conducted thoroughly from all scientific databases to avoid unnecessary clutter. This study summarizes and discusses different types of deep learning algorithms applied in air quality forecasting, including the theoretical backgrounds, hyperparameters, applications and limitations. Hybrid deep learning with data decomposition, optimization algorithm and spatiotemporal models are also presented to highlight those techniques' effectiveness in tackling the drawbacks of individual deep learning models. It is clearly stated that hybrid deep learning was able to forecast future air quality with higher accuracy than individual models. At the end of the study, some possible research directions are suggested for future model development. The main objective of this review study is to provide a comprehensive literature summary of deep learning applications in time series air quality forecasting that may benefit interested researchers for subsequent research.

Integration of complete ensemble empirical mode decomposition with deep long short-term memory model for particulate matter concentration prediction

The atmospheric particulate matter (PM) with a diameter of 2.5 μm or less (PM2.5) is one of the key indicators of air pollutants. Accurate prediction of PM2.5 concentration is very important for air pollution monitoring and public health management. However, the presence of noise in PM2.5 data series is a major challenge of its accurate prediction. A novel hybrid PM2.5 concentration prediction model is proposed in this study by combining complete ensemble empirical mode decomposition (CEEMD) method, Pearson's correlation analysis, and a deep long short-term memory (LSTM) method. CEEMD was employed to decompose historical PM2.5 concentration data to different frequencies in order to enhance the timing characteristics of data. Pearson's correlation was used to screen the different frequency intrinsic-mode functions of decomposed data. Finally, the filtered enhancement data were inputted to a deep LSTM network with multiple hidden layers for training and prediction. The results evidenced the potential of the CEEMD-LSTM hybrid model with a prediction accuracy of approximately 80% and model convergence after 700 training epochs. The secondary screening of Pearson's correlation test improved the model (CEEMD-Pearson) accuracy up to 87% but model convergence after 800 epochs. The hybrid model combining CEEMD-Pearson with the deep LSTM neural network showed a prediction accuracy of nearly 90% and model convergence after 650 interactions. The results provide a clear indication of higher prediction accuracy of PM2.5 with less computation time through hybridization of CEEMD-Pearson with deep LSTM models and its potential to be employed for air pollution monitoring.

Comparing i-Tree Eco Estimates of Particulate Matter Deposition with Leaf and Canopy Measurements in an Urban Mediterranean Holm Oak Forest

Trees and urban forests remove particulate matter (PM) from the air through the deposition of particles on the leaf surface, thus helping to improve air quality and reduce respiratory problems in urban areas. Leaf deposited PM, in turn, is either resuspended back into the atmosphere, washed off during rain events or transported to the ground with litterfall. The net amount of PM removed depends on crown and leaf characteristics, air pollution concentration, and weather conditions, such as wind speed and precipitation. Many existing deposition models, such as i-Tree Eco, calculate PM_2.5 removal using a uniform deposition velocity function and resuspension rate for all tree species, which vary based on leaf area and wind speed. However, model results are seldom validated with experimental data. In this study, we compared i-Tree Eco calculations of PM_2.5 deposition with fluxes determined by eddy covariance assessments (canopy scale) and particulate matter accumulated on leaves derived from measurements of vacuum/filtration technique as well as scanning electron microscopy combined with energy-dispersive X-ray spectroscopy (leaf scale). These investigations were carried out at the Capodimonte Royal Forest in Naples. Modeled and measured fluxes showed good overall agreement, demonstrating that net deposition mostly happened in the first part of the day when atmospheric PM concentration is higher, followed by high resuspension rates in the second part of the day, corresponding with increased wind speeds. The sensitivity analysis of the model parameters showed that a better representation of PM deposition fluxes could be achieved with adjusted deposition velocities. It is also likely that the standard assumption of a complete removal of particulate matter, after precipitation events that exceed the water storage capacity of the canopy (Ps), should be reconsidered to better account for specific leaf traits. These results represent the first validation of i-Tree Eco PM removal with experimental data and are a starting point for improving the model parametrization and the estimate of particulate matter removed by urban trees.

Dry Deposition of Atmospheric Aerosols: Approaches, Observations, and Mechanisms

Aerosols are liquid or solid particles suspended in the atmosphere, typically with diameters on the order of nanometers to microns. These particles impact air quality and the radiative balance of the planet. Dry deposition is a key process for the removal of aerosols from the atmosphere and plays an important role in controlling the lifetime of atmospheric aerosols. Dry deposition is driven by turbulence and shows a strong dependence on particle size. This review summarizes the mechanisms behind aerosol dry deposition, including measurement approaches, field observations, and modeling studies. We identify several gaps in the literature, including deposition over the cryosphere (i.e., snow and ice surfaces) and the ocean; in addition, we highlight new techniques to measure black carbon fluxes. While recent advances in aerosol instrumentation have enhanced our understanding of aerosol sources and chemistry, dry deposition and other loss processes remain poorly investigated.

Ambient Air Quality as a Condition of Effective Healthcare Therapy on the Example of Selected Polish Health Resorts

This article discusses the importance of air quality for the organization and functioning of health resorts. Ten different types of resorts located in various regions of Poland were compared in terms PM10 concentration. Additionally, comparative analysis of the high-PM10 episodes was performed in three urban agglomerations located near the analyzed health resorts. The article also discusses formal, legal, and economic instruments that are the basis for legislative actions as tools for managing the air quality in the selected resorts. The analysis of the average annual concentrations in 2015–2019 did not show any exceedances of the PM10 limit value for any of the health resorts studied. High PM10 concentration values in 2018 were recorded for the number of days in exceedance of the limit value, especially in the health resorts of Uniejów, Ciechocinek, and Szczawno-Zdrój. Health resorts located in the south of Poland were identified as the most at risk in terms of the occurrence of limit value exceedances, information, and alert thresholds. It was concluded that the implementation of the so called “anti-smog” resolutions, including the development of financial support for changing the heating system to eliminate coal boilers and furnaces, is absolutely necessary for air quality improvement.