Sources of high PM2.5 concentrations in Milan, Northern Italy: molecular marker data and CMB modelling

Does digital infrastructure construction impact urban carbon emission reduction? Evidence from China's smart city construction

As the cornerstone of the digital economy, the construction of digital infrastructure plays a crucial role in promoting China's high-quality economic growth.. Against the backdrop of the "dual-carbon" goals, the development of digital infrastructure will provide new momentum for carbon emissions reduction in urban areas. This study utilizes unbalanced panel data from 277 prefecture-level cities in China between 2008 and 2019, treating the smart city construction as a quasi-natural experiment, to systematically evaluate the impact of the pilot construction of smart city on urban carbon emissions intensity. The research findings reveal that the construction of the smart city has significantly contributed to the reduction of urban carbon emissions intensity, indicating that digital infrastructure contributes to urban carbon emission reduction. The reduction of carbon emissions resulting from smart city construction is particularly significant in the East and Central regions., as well as regions with high financial development levels, regions with high human capital levels and non resource-based cities. The construction of the smart city primarily achieves the reduction of urban carbon emissions intensity through two main pathways: improving the penetration rate of digital infrastructure and enhancing technological innovation capability. Therefore, this study recommends that local governments strengthen the integration and penetration of digital infrastructure with traditional industries, foster urban innovation vitality, and accelerate the transformation towards green and low-carbon cities.

Determining the Aethalometer multiple scattering enhancement factor C from the filter loading parameter

Light-absorbing aerosols heat the atmosphere; an accurate quantification of their absorption coefficient is mandatory. However, standard reference instruments (CAPS, MAAP, PAX, PTAAM) are not always available at each measuring site around the world. By integrating all previous published studies concerning the Aethalometers, the AE33 filter loading parameter, provided by the dual-spot algorithm, were used to determine the multiple scattering enhancement factor from the Aethalometer itself (hereinafter CAE) on an yearly and a monthly basis. The method was developed in Milan, where Aethalometer measurements were compared with MAAP data; the comparison showed a good agreement in terms of equivalent black carbon (R2 = 0.93; slope = 1.02 and a negligible intercept = 0.12 μg m-3) leading to a yearly experimental multiple scattering enhancement factor of 2.51 ± 0.04 (hereinafter CMAAP). On a yearly time base the CAE values obtained using the new approach was 2.52 ± 0.01, corresponding to the experimental one (CMAAP). Considering the seasonal behavior, higher experimental CMAAP and computed CAE values were found in summer (2.83 ± 0.12) whereas, the lower ones in winter/early-spring (2.37 ± 0.03), in agreement with the single scattering albedo behavior in the Po Valley. Overall, the agreement between the experimental CMAAP and CAE showed a root mean squared error (RMSE) of just 0.038 on the CMAAP prediction, characterized by a slope close to 1 (1.001 ± 0.178), a negligible intercept (-0.002 ± 0.455) and a high degree of correlation (R2 = 0.955). From an environmental point of view, the application of a dynamic (space/time) determination of CAE increases the accuracy of the aerosol heating rate (compared to applying a fixed C value) up to 16 % solely in Milan, and to 114 % when applied in the Arctic at 80°N.

The Impact of Russia-Ukraine geopolitical conflict on the air quality and toxicological properties of ambient PM2.5 in Milan, Italy

The geopolitical conflict between Russia and Ukraine has disrupted Europe's natural gas supplies, driving up gas prices and leading to a shift towards biomass for residential heating during colder months. This study assessed the consequent air quality and toxicological impacts in Milan, Italy, focusing on fine particulate matter (PM2.5, dp < 2.5 μm) emissions. PM2.5 samples were analyzed for their chemical composition and assessed for their oxidative potential using the dithiothreitol (DTT) assay across three periods reflecting residential heating deployment (RHD): pre-RHD, intra-RHD, and post-RHD periods. During the intra-RHD period, PM2.5 levels were significantly higher than those in other periods, with concentrations reaching 57.94 ± 7.57 μg/m3, indicating a deterioration in air quality. Moreover, levoglucosan was 9.2 times higher during the intra-RHD period compared to the pre-RHD period, correlating with elevated levels of elemental carbon (EC) and polycyclic aromatic hydrocarbons (PAHs). These findings were compared with previous local studies before the conflict, underscoring a significant rise in biomass-related emissions. DTT assay levels during the intra-RHD were 2.1 times higher than those observed during the same period in 2022, strongly correlating with biomass burning emissions. Our findings highlight the necessity for policies to mitigate the indirect health effects of increased biomass burning emissions due to the energy crisis triggered by the geopolitical conflict.

Effects of PM2.5 Exposure on the ACE/ACE2 Pathway: Possible Implication in COVID-19 Pandemic

Particulate matter (PM) is a harmful component of urban air pollution and PM2.5, in particular, can settle in the deep airways. The RAS system plays a crucial role in the pathogenesis of pollution-induced inflammatory diseases: the ACE/AngII/AT1 axis activates a pro-inflammatory pathway counteracted by the ACE2/Ang(1-7)/MAS axis, which in turn triggers an anti-inflammatory and protective pathway. However, ACE2 acts also as a receptor through which SARS-CoV-2 penetrates host cells to replicate. COX-2, HO-1, and iNOS are other crucial proteins involved in ultrafine particles (UFP)-induced inflammation and oxidative stress, but closely related to the course of the COVID-19 disease. BALB/c male mice were subjected to PM2.5 sub-acute exposure to study its effects on ACE2 and ACE, COX-2, HO-1 and iNOS proteins levels, in the main organs concerned with the pathogenesis of COVID-19. The results obtained show that sub-acute exposure to PM2.5 induces organ-specific modifications which might predispose to greater susceptibility to severe symptomatology in the case of SARS-CoV-2 infection. The novelty of this work consists in using a molecular study, carried out in the lung but also in the main organs involved in the disease, to analyze the close relationship between exposure to pollution and the pathogenesis of COVID-19.

Cleaning the Flue in Wood-Burning Stoves Is a Key Factor in Reducing Household Air Pollution

In experimental settings, replacing old wood stoves with new wood stoves results in reduced personal exposure to household air pollution. We tested this assumption by measuring PM2.5 and levoglucosan concentrations inside homes and correlated them with wood stove age. Methods: Thirty homes in the Albuquerque, NM area were monitored over a seven-day period using in-home particulate monitors placed in a common living area during the winter months. Real-time aerosol monitoring was performed, and filter samples were analyzed gravimetrically to calculate PM2.5 concentrations and chemically to determine concentrations of levoglucosan. A linear regression model with backward stepwise elimination was performed to determine the factors that would predict household air pollution measures. Results: In this sample, 73.3% of the households used wood as their primary source of heating, and 60% burned daily or almost daily. The mean burn time over the test week was 50 ± 38 h, and only one household burned wood 24/day (168 h). The average PM2.5 concentration (standard deviation) for the 30 homes during the seven-day period was 34.6 µg/m3 (41.3 µg/m3), and median (min, max) values were 15.5 µg/m3 (7.3 µg/m3, 193 µg/m3). Average PM2.5 concentrations in 30 homes ranged from 0−15 μg/m3 to >100 μg/m3. Maximum PM2.5 concentrations ranged from 100−200 μg/m3 to >3000 μg/m3. The levoglucosan levels showed a linear correlation with the total PM2.5 collected by the filters (R2 = 0.92). However, neither mean nor peak PM2.5 nor levoglucosan levels were correlated with the age (10.85 ± 8.54 years) of the wood stove (R2 ≤ 0.07, p > 0.23). The final adjusted linear regression model showed that average PM2.5 was associated with reports of cleaning the flue with a beta estimate of 35.56 (3.47−67.65) and R2 = 0.16 (p = 0.04). Discussion: Cleaning the flue and not the wood stove age was associated with household air pollution indices. Education on wood stove maintenance and safe burning practices may be more important in reducing household air pollution than the purchase of new stoves.

Impacts of exposure to air pollution, radon and climate drivers on the COVID-19 pandemic in Bucharest, Romania: A time series study

During the ongoing global COVID-19 pandemic disease, like several countries, Romania experienced a multiwaves pattern over more than two years. The spreading pattern of SARS-CoV-2 pathogens in the Bucharest, capital of Romania is a multi-factorial process involving among other factors outdoor environmental variables and viral inactivation. Through descriptive statistics and cross-correlation analysis applied to daily time series of observational and geospatial data, this study aims to evaluate the synergy of COVID-19 incidence and lethality with air pollution and radon under different climate conditions, which may exacerbate the coronavirus' effect on human health. During the entire analyzed period 1 January 2020-21 December 2021, for each of the four COVID-19 waves were recorded different anomalous anticyclonic synoptic meteorological patterns in the mid-troposphere, and favorable stability conditions during fall-early winter seasons for COVID-19 disease fast-spreading, mostly during the second, and the fourth waves. As the temporal pattern of airborne SARS-CoV-2 and its mutagen variants is affected by seasonal variability of the main air pollutants and climate parameters, this paper found: 1) the daily outdoor exposures to air pollutants (particulate matter PM2.5 and PM10, nitrogen dioxide-NO₂, sulfur dioxide-SO₂, carbon monoxide-CO) and radon - ²²²Rn, are directly correlated with the daily COVID-19 incidence and mortality, and may contribute to the spread and the severity of the pandemic; 2) the daily ground ozone-O₃ levels, air temperature, Planetary Boundary Layer height, and surface solar irradiance are anticorrelated with the daily new COVID-19 incidence and deaths, averageingful for spring-summer periods. Outdoor exposure to ambient air pollution associated with radon is a non-negligible driver of COVID-19 transmission in large metropolitan areas, and climate variables are risk factors in spreading the viral infection. The findings of this study provide useful information for public health authorities and decision-makers to develop future pandemic diseases strategies in high polluted metropolitan environments.

Transport Infrastructure, High-Quality Development and Industrial Pollution: Fresh Evidence from China

To achieve high-quality development, transport infrastructure will play a crucial role in China’s economic growth, but its damage to the ecological environment has not been paid enough attention. This study was based on panel data for 30 Chinese provinces for the period of 2004–2017. A comprehensive index system for high-quality development based on the new development concept was developed. This high-quality development index used the entropy weight method and integrated transport infrastructure, high-quality development, and industrial pollution into a comprehensive framework, and systematically examined the effects of transport infrastructure and high-quality development on industrial pollution emissions. It was found that transport infrastructure significantly contributed to industrial pollution emissions, and there was a regional heterogeneity and time lag, with high-quality development and industrial pollution having an inverted “U”-shaped relationship. Further analysis showed that transport infrastructure significantly affected high-quality development and industrial pollution through industrial agglomeration, reduced the inhibitory effect on high-quality development by promoting industrial agglomeration, and reduced industrial pollution emissions by promoting industrial agglomeration.

Machine Learning-Based Approach Using Open Data to Estimate PM2.5 over Europe

Air pollution is currently considered one of the most serious problems facing humans. Fine particulate matter with a diameter smaller than 2.5 micrometres (PM2.5) is a very harmful air pollutant that is linked with many diseases. In this study, we created a machine learning-based scheme to estimate PM2.5 using various open data such as satellite remote sensing, meteorological data, and land variables to increase the limited spatial coverage provided by ground-monitors. A space-time extremely randomised trees model was used to estimate PM2.5 concentrations over Europe, this model achieved good results with an out-of-sample cross-validated R2 of 0.69, RMSE of 5 μg/m3, and MAE of 3.3 μg/m3. The outcome of this study is a daily full coverage PM2.5 dataset with 1 km spatial resolution for the three-year period of 2018–2020. We found that air quality improved throughout the study period over all countries in Europe. In addition, we compared PM2.5 levels during the COVID-19 lockdown during the months March–June with the average of the previous 4 months and the following 4 months. We found that this lockdown had a positive effect on air quality in most parts of the study area except for the United Kingdom, Ireland, north of France, and south of Italy. This is the first study that depends only on open data and covers the whole of Europe with high spatial and temporal resolutions. The reconstructed dataset will be published under free and open license and can be used in future air quality studies.

Haze management: is urban public transportation priority effective?

Public transportation is often considered as a green travel mode to alleviate the negative externalities such as traffic congestion and haze pollution generated from transport. However, is prioritizing urban public transportation actually conducive to haze emission reduction? In this study, considering special emphasis on the cumulative effect of haze, a dynamic panel model is constructed to analyze and quantify the impact of public transportation on haze pollution by using the data of 284 cities in China, and the heterogeneity of the impact in cities with different pollution levels is examined. Several interesting findings are derived from the empirical results. First, the development of urban public transportation can significantly alleviate urban haze pollution. Second, the haze reduction effect of public transportation in cities with different pollution levels is non-universal. Comparatively speaking, the haze reduction effect of public transportation in lightly polluted cities is more evident than that in heavily polluted cities. Therefore, in order to reduce haze pollution in a more effective manner, China should continue to promote urban public transportation priority strategy. Moreover, the government should also formulate differentiated traffic development strategies to effectively alleviate the urban traffic burdens.

Spatiotemporal Variation in Groundwater Quality and Source Apportionment along the Ye River of North China Using the PMF Model

Groundwater quality deterioration has attracted widespread concern in China. In this research, the water quality index (WQI) and a positive matrix factorization (PMF) model were used to assess groundwater quality and identify pollution sources in the Ye River area of northern China. Research found that TH, SO₄²⁻, and NO₃⁻ were the main groundwater pollution factors in the Ye River area, since their exceeding standard rates were 78.13, 34.38, and 59.38%, respectively. The main groundwater hydrochemical type has changed from HCO₃-Ca(Mg) to HCO₃·SO₄-Ca(Mg). These data indicated that the groundwater quality was affected by anthropogenic activities. Spatial variation in groundwater quality was mainly influenced by land use, whereas temporal variation was mainly controlled by rainfall. The WQI indicated that the groundwater quality was better in the flood season than in the dry season due to the diluting effect of rainfall runoff. Notably, farmland groundwater quality was relatively poor as it was affected by various pollution sources. Based on the PMF model, the main groundwater pollution sources were domestic sewage (52.4%), industrial wastewater (24.1%), and enhanced water–rock interaction induced by intensely exploited groundwater (23.6%) in the dry season, while in the flood season they were domestic sewage and water–rock interaction (49.6%), agriculture nonpoint pollution (26.1%), and industrial wastewater and urban nonpoint pollution (23.9%). In addition, the mean contribution of domestic sewage and industrial sewage to sampling sites in the dry season (1489 and 322.5 mg/L, respectively) were higher than that in the flood season (1158 and 273.6 mg/L, respectively). To sum up, the point sources (domestic sewage and industrial wastewater) remain the most important groundwater pollution sources in this region. Therefore, the local government should enhance the sewage treatment infrastructure and exert management of fertilization strategies to increase the fertilizer utilization rate and prevent further groundwater quality deterioration.

Assessing the impact of air pollution and climate seasonality on COVID-19 multiwaves in Madrid, Spain

While the COVID-19 pandemic is still in progress, being under the fifth COVID-19 wave in Madrid, over more than one year, Spain experienced a four wave pattern. The transmission of SARS-CoV-2 pathogens in Madrid metropolitan region was investigated from an urban context associated with seasonal variability of climate and air pollution drivers. Based on descriptive statistics and regression methods of in-situ and geospatial daily time series data, this study provides a comparative analysis between COVID-19 waves incidence and mortality cases in Madrid under different air quality and climate conditions. During analyzed period 1 January 2020-1 July 2021, for each of the four COVID-19 waves in Madrid were recorded anomalous anticyclonic synoptic meteorological patterns in the mid-troposphere and favorable stability conditions for COVID-19 disease fast spreading. As airborne microbial temporal pattern is most affected by seasonal changes, this paper found: 1) a significant negative correlation of air temperature, Planetary Boundary Layer height, and surface solar irradiance with daily new COVID-19 incidence and deaths; 2) a similar mutual seasonality with climate variables of the first and the fourth COVID-waves from spring seasons of 2020 and 2021 years. Such information may help the health decision makers and public plan for the future.

Effects of Wood Smoke Constituents on Mucin Gene Expression in Mice and Human Airway Epithelial Cells and on Nasal Epithelia of Subjects with a Susceptibility Gene Variant in Tp53

BACKGROUND

Exposure to wood smoke (WS) increases the risk for chronic bronchitis more than exposure to cigarette smoke (CS), but the underlying mechanisms are unclear.

OBJECTIVE

The effect of WS and CS on mucous cell hyperplasia in mice and in human primary airway epithelial cells (AECs) was compared with replicate the findings in human cohorts. Responsible WS constituents were identified to better delineate the pathway involved, and the role of a tumor protein p53 (Tp53) gene polymorphism was investigated.

METHODS

Mice and primary human AECs were exposed to WS or CS and the signaling receptor and pathway were identified using short hairpin structures, small molecule inhibitors, and Western analyses. Mass spectrometric analysis was used to identify active WS constituents. The role of a gene variant in Tp53 that modifies proline to arginine was examined using nasal brushings from study participants in the Lovelace Smokers Cohort, primary human AECs, and mice with a modified Tp53 gene.

RESULTS

WS at 25-fold lower concentration than CS increased mucin expression more efficiently in mice and in human AECs in a p53 pathway-dependent manner. Study participants who were homozygous for p53 arginine compared with the proline variant showed higher mucin 5AC (MUC5AC) mRNA levels in nasal brushings if they reported WS exposure. The WS constituent, oxalate, increased MUC5AC levels similar to the whole WS extract, especially in primary human AECs homozygous for p53 arginine, and in mice with a modified Tp53 gene. Further, the anion exchange protein, SLC26A9, when reduced, enhanced WS- and oxalate-induced mucin expression.

DISCUSSION

The potency of WS compared with CS in inducing mucin expression may explain the increased risk for chronic bronchitis in participants exposed to WS. Identification of the responsible compounds could help estimate the risk of pollutants in causing chronic bronchitis in susceptible individuals and provide strategies to improve management of lung diseases. https://doi.org/10.1289/EHP9446.

Monthly Global Estimates of Fine Particulate Matter and Their Uncertainty

Annual global satellite-based estimates of fine particulate matter (PM2.5) are widely relied upon for air-quality assessment. Here, we develop and apply a methodology for monthly estimates and uncertainties during the period 1998-2019, which combines satellite retrievals of aerosol optical depth, chemical transport modeling, and ground-based measurements to allow for the characterization of seasonal and episodic exposure, as well as aid air-quality management. Many densely populated regions have their highest PM2.5 concentrations in winter, exceeding summertime concentrations by factors of 1.5-3.0 over Eastern Europe, Western Europe, South Asia, and East Asia. In South Asia, in January, regional population-weighted monthly mean PM2.5 concentrations exceed 90 μg/m3, with local concentrations of approximately 200 μg/m3 for parts of the Indo-Gangetic Plain. In East Asia, monthly mean PM2.5 concentrations have decreased over the period 2010-2019 by 1.6-2.6 μg/m3/year, with decreases beginning 2-3 years earlier in summer than in winter. We find evidence that global-monitored locations tend to be in cleaner regions than global mean PM2.5 exposure, with large measurement gaps in the Global South. Uncertainty estimates exhibit regional consistency with observed differences between ground-based and satellite-derived PM2.5. The evaluation of uncertainty for agglomerated values indicates that hybrid PM2.5 estimates provide precise regional-scale representation, with residual uncertainty inversely proportional to the sample size.

Characteristics and Extent of Particulate Matter Emissions of a Ropeway Public Mobility System in the City Center of Perugia (Central Italy)

Minimetrò (MM) is a ropeway public mobility system that has been in operation in the city of Perugia for about ten years to integrate with urban mobility and lighten vehicular traffic in the historic city center. The purpose of this work was to evaluate the impact of MM as a source of pollutants in the urban context, and the exposure of people in the cabins and the platforms along the MM line. These topics have been investigated by means of intensive measurement and sampling campaigns performed in February and June 2015 on three specific sites of the MM line representative of different sources and levels of urban pollution. Stationary and dynamic measurements of particle size distribution, nanoparticle and black carbon aerosol number and mass concentrations measurements were performed by means of different bench and portable instruments. Aerosol sampling was carried out using low volume and high-volume aerosol samplers, and the samples nalysed by off-line methods. Results show that MM is a considerable source of atmospheric particulate matter having characteristics very similar to those of the common urban road dust in Perugia. In the lack of clear indications on road dust effect, the contribution of MM to the aerosol in Perugia cannot be neglected.

Consistent determination of the heating rate of light-absorbing aerosol using wavelength- and time-dependent Aethalometer multiple-scattering correction

Accurate and temporally consistent measurements of light absorbing aerosol (LAA) heating rate (HR) and of its source apportionment (fossil-fuel, FF; biomass-burning, BB) and speciation (black and brown Carbon; BC, BrC) are needed to evaluate LAA short-term climate forcing. For this purpose, wavelength- and time-dependent accurate LAA absorption coefficients are required. HR was experimentally determined and apportioned (sources/species) in the EMEP/ACTRIS/COLOSSAL-2018 winter campaign in Milan (urban-background site). Two Aethalometers (AE31/AE33) were installed together with a MAAP, CPC, OPC, a low volume sampler (PM_2.5) and radiation instruments. AE31/AE33 multiple-scattering correction factors (C) were determined using two reference systems for the absorption coefficient: 1) 5-wavelength PP_UniMI with low time resolution (12 h, applied to PM_2.5 samples); 2) timely-resolved MAAP data at a single wavelength. Using wavelength- and time-independent C values for the AE31 and AE33 obtained with the same reference device, the total HR showed a consistency (i.e. reproducibility) with average values comparable at 95% probability. However, if different reference devices/approaches are used, i.e. MAAP is chosen as reference instead of a PP_UniMI, the HR can be overestimated by 23-30% factor (by both AE31/AE33). This became more evident focusing on HR apportionment: AE33 data (corrected by a wavelength- and time-independent C) showed higher HR_FF (+24 ± 1%) and higher HR_BC (+10 ± 1%) than that of AE31. Conversely, HR_BB and HR_BrC were -28 ± 1% and -29 ± 1% lower for AE33 compared to AE31. These inconsistencies were overcome by introducing a wavelength-dependent C_λ for both AE31 and AE33, or using multi-wavelength apportionment methods, highlighting the need for further studies on the influence of wavelength corrections for HR determination. Finally, the temporally-resolved determination of C resulted in a diurnal cycle of the HR not statistically different whatever the source- speciation- apportionment used.

Dustfall-bound polycyclic aromatic hydrocarbons (PAHs) over the fifth largest Chinese lake: residual levels, source apportionment, and correlations with suspended particulate matter (SPM)-bound PAHs in water

Residual levels and temporal-spatial distribution characteristics of polycyclic aromatic hydrocarbons (PAHs) in dustfall were studied at the seasonal scale between June 2010 and May 2011 in the fifth largest shallow lake in China. PAHs flux of atmospheric deposition and the impact on the PAHs in the lake water column were estimated. The major sources of PAHs were identified by multiple methods. We found that (1) the seasonal residual levels of 16 priority controlled PAHs (PAH₁₆) were spring (8.89 ± 3.93 μg g^-1) > summer (6.68 ± 4.31 μg g^-1) > winter (6.06 ± 2.95 μg g^-1) > autumn (3.55 ± 2.21 μg g^-1). (2) Significant positive correlations were found between the PAH levels in the dustfall and the suspended particle material (SPM) content, as well as between the deposition flux and the PAH content in the water in all four seasons. (3) Vehicle emissions, coal combustion, biomass combustion, and coke ovens were the four major sources in Lake Chaohu, accounting for 12.7%, 40.9%, 14.5%, and 31.9% of the total PAHs, respectively. (4) Compared to long-distance trajectories, short-distance trajectories played a more important role in the external sources of atmospheric PAHs in the region of Lake Chaohu.

Exploring the effect of urban traffic development on PM2.5 pollution in emerging economies: fresh evidence from China

Urban traffic congestion and haze pollution have become the main obstacles to the development of most cities in emerging economies. It is not clear how urban traffic development processes impact on PM_2.5 concentration for the cities of emerging economies. Motivated by exploring the relationship between urban traffic development and PM_2.5 pollution, 30 provinces in China (a representative emerging economy) from 2007 to 2016 were taken as examples, and threshold regression model and geographically temporally weighted regression model were used to explore the nonlinear relationship and their spatio-temporal heterogeneity. These empirical researches demonstrated that the impact of urban traffic development on PM_2.5 pollution has a significant threshold effect. That is, when the road area crosses the threshold, it will significantly aggravate the regional PM_2.5 pollution. Meanwhile, regional economic development also shows a significant threshold effect. Moreover, the relationship between urban traffic development and PM_2.5 pollution in various Chinese provinces presents significant spatial heterogeneity. Specifically, the Chinese provinces are divided into four categories, and urban planning should be designed for different types for the sustainable development of the economy and environment. Our results not only contribute to advancing the existing literature, but also merit particular attention from urban planners in emerging economies.

Exploring the linkage between seasonality of environmental factors and COVID-19 waves in Madrid, Spain

Like several countries, Spain experienced a multi wave pattern of COVID-19 pandemic over more than one year period, between spring 2020 and spring 2021. The transmission of SARS-CoV-2 pandemics is a multi-factorial process involving among other factors outdoor environmental variables and viral inactivation.This study aims to quantify the impact of climate and air pollution factors seasonality on incidence and severity of COVID-19 disease waves in Madrid metropolitan region in Spain. We employed descriptive statistics and Spearman rank correlation tests for analysis of daily in-situ and geospatial time-series of air quality and climate data to investigate the associations with COVID-19 incidence and lethality in Madrid under different synoptic meteorological patterns. During the analyzed period (1 January 2020-28 February 2021), with one month before each of three COVID-19 waves were recorded anomalous anticyclonic circulations in the mid-troposphere, with positive anomalies of geopotential heights at 500 mb and favorable stability conditions for SARS-CoV-2 fast diffusion. In addition, the results reveal that air temperature, Planetary Boundary Layer height, ground level ozone have a significant negative relationship with daily new COVID-19 confirmed cases and deaths. The findings of this study provide useful information to the public health authorities and policymakers for optimizing interventions during pandemics.

An investigation for airflow and deposition of PM2.5 contaminated with SAR-CoV-2 virus in healthy and diseased human airway

This study is motivated by the amplified transmission rates of the SAR-CoV-2 virus in areas with high concentrations of fine particulates (PM_2.5) as reported in northern Italy and Mexico. To develop a deeper understanding of the contribution of PM_2.5 in the propagation of the SAR-CoV-2 virus in the population, the deposition patterns and efficiencies (DEs) of PM_2.5 laced with the virus in healthy and asthmatic airways are studied. Physiologically correct 3-D models for generations 10-12 of the human airways are applied to carry out a numerical analysis of two-phase flow for full breathing cycles. Two concentrations of PM_2.5 are applied for the simulation, i.e., 30 μg⋅m^-3 and 80 μg⋅m^-3 for three breathing statuses, i.e., rest, light exercise, and moderate activity. All the PM_2.5 injected into the control volume is assumed to be 100% contaminated with the SAR-CoV-2 virus. Skewed air-flow phenomena at the bifurcations are proportional to the Reynolds number at the inlet, and their intensity in the asthmatic airway exceeded that of the healthy one. Upon exhalation, two peak air-flow vectors from daughter branches combine to form one big vector in the parent generation. Asthmatic airway models has higher deposition efficiencies (DEs) for contaminated PM_2.5 as compared to the healthy one. Higher DEs arise in the asthmatic airway model due to complex secondary flows which increase the impaction of contaminated PM_2.5 on airways' walls.

PM2.5 Pollution Strongly Predicted COVID-19 Incidence in Four High-Polluted Urbanized Italian Cities during the Pre-Lockdown and Lockdown Periods

BACKGROUND

The coronavirus disease in 2019 (COVID-19) heavily hit Italy, one of Europe's most polluted countries. The extent to which PM pollution contributed to COVID-19 diffusion is needing further clarification. We aimed to investigate the particular matter (PM) pollution and its correlation with COVID-19 incidence across four Italian cities: Milan, Rome, Naples, and Salerno, during the pre-lockdown and lockdown periods.

METHODS

We performed a comparative analysis followed by correlation and regression analyses of the daily average PM10, PM2.5 concentrations, and COVID-19 incidence across four cities from 1 January 2020 to 8 April 2020, adjusting for several factors, taking a two-week time lag into account.

RESULTS

Milan had significantly higher average daily PM10 and PM2.5 levels than Rome, Naples, and Salerno. Rome, Naples, and Salerno maintained safe PM10 levels. The daily PM2.5 levels exceeded the legislative standards in all cities during the entire period. PM2.5 pollution was related to COVID-19 incidence. The PM2.5 levels and sampling rate were strong predictors of COVID-19 incidence during the pre-lockdown period. The PM2.5 levels, population's age, and density strongly predicted COVID-19 incidence during lockdown.

CONCLUSIONS

Italy serves as a noteworthy paradigm illustrating that PM2.5 pollution impacts COVID-19 spread. Even in lockdown, PM2.5 levels negatively impacted COVID-19 incidence.

Toxic Organic Contaminants in Airborne Particles: Levels, Potential Sources and Risk Assessment

In the last years, many studies have focused on risk assessment of exposure of workers to airborne particulate matter (PM). Several studies indicate a strong correlation between PM and adverse health outcomes, as a function of particle size. In the last years, the study of atmospheric particulate matter has focused more on particles less than 10 μm or 2.5 μm in diameter; however, recent studies identify in particles less than 0.1 μm the main responsibility for negative cardiovascular effects. The present paper deals with the determination of 66 organic compounds belonging to six different classes of persistent organic pollutants (POPs) in the ultrafine, fine and coarse fractions of PM (PM < 0.1 µm; 0.1 < PM < 2.5 µm and 2.5 < PM < 10 µm) collected in three outdoor workplaces and in an urban outdoor area. Data obtained were analyzed with principal component analysis (PCA), in order to underline possible correlation between sites and classes of pollutants and characteristic emission sources. Emission source studies are, in fact, a valuable tool for both identifying the type of emission source and estimating the strength of each contamination source, as useful indicator of environment healthiness. Moreover, both carcinogenic and non-carcinogenic risks were determined in order to estimate human health risk associated to study sites. Risk analysis was carried out evaluating the contribution of pollutant distribution in PM size fractions for all the sites. The results highlighted significant differences between the sites and specific sources of pollutants related to work activities were identified. In all the sites and for all the size fractions of PM both carcinogenic and non-carcinogenic risk values were below acceptable and safe levels of risks recommended by the regulatory agencies.

The impact of stay-home policies during Coronavirus-19 pandemic on the chemical and toxicological characteristics of ambient PM2.5 in the metropolitan area of Milan, Italy

The goal of this study was to characterize changes in components and toxicological properties of PM2.5 during the nationwide 2019-Coronavirus (COVID-19) lockdown restrictions in Milan, Italy. Time-integrated PM2.5 filters were collected at a residential site in Milan metropolitan area from April 11th to June 3rd at 2020, encompassing full-lockdown (FL), the followed partial-lockdown (PL2), and full-relaxation (FR) periods of COVID-19 restrictions. The collected filters were analyzed for elemental and organic carbon (EC/OC), water-soluble organic carbon (WSOC), individual organic species (e.g., polycyclic aromatic hydrocarbons (PAHs), and levoglucosan), and metals. According to online data, nitrogen dioxide (NO2) and benzene (C6H6) levels significantly decreased during the entire COVID-19 period compared to the same time span in 2019, mainly due to the government-backed shutdowns and curtailed road traffic. Similarly, with a few exceptions, surrogates of tailpipe emissions (e.g., traffic-associated PAHs) as well as re-suspended road dust (e.g., Fe, Mn, Cu, Cr, and Ti) were relatively lower during FL and PL2 periods in comparison with year 2019, whereas an increasing trend in mass concentration of mentioned species was observed from FL to PL2 and FR phases due to the gradual lifting of lockdown restrictions. In contrast, comparable concentrations of ambient PM2.5 and black carbon (BC) between lockdown period and the same time span in 2019 were attributed to the interplay between decreased road traffic and elevated domestic biomass burning as a result of adopted stay-home strategies. Finally, the curtailed road traffic during FL and PL2 periods led to ~25% drop in the PM2.5 oxidative potential (measured via 2',7'-dichlorodihydrofluorescein (DCFH) and dithiothreitol (DTT) assays) with respect to the FR period as well as the same time span in 2019. The results of this study provide insights into the changes in components and oxidative potential of PM2.5 in the absence of road traffic during COVID-19 restrictions.

Impact of transport of fine and ultrafine particles from open biomass burning on air quality during 2019 Bangkok haze episode

Transboundary and domestic aerosol transport during 2018-2019 affecting Bangkok air quality has been investigated. Physicochemical characteristics of size-segregated ambient particles down to nano-particles collected during 2017 non-haze and 2018-2019 haze periods were analyzed. The average PM_2.5 concentrations at KU and KMUTNB sites in Bangkok, Thailand during the haze periods were about 4 times higher than in non-haze periods. The highest average organic carbon and elemental carbon concentrations were 4.6 ± 2.1 µg/m³ and 1.0 ± 0.4 µg/m³, respectively, in PM_0.5-1.0 range at KU site. The values of OC/EC and char-EC/soot-EC ratios in accumulation mode particles suggested the significant influence of biomass burning, while the nuclei and coarse mode particles were from mixed sources. PAH concentrations during 2018-2019 haze period at KU and KMUTNB were 3.4 ± 0.9 ng/m³ and 1.8 ± 0.2 ng/m³, respectively. The PAH diagnostic ratio of PM_2.5 also suggested the main contributions were from biomass combustion. This is supported by the 48-hrs backward trajectory simulation. The higher PM_2.5 concentrations during 2018-2019 haze period are also associated with the meteorological conditions that induce thermal inversions and weak winds in the morning and evening. Average values of benzo(a)pyrene toxic equivalency quotient during haze period were about 3-6 times higher than during non-haze period. This should raise a concern of potential human health risk in Bangkok and vicinity exposing to fine and ultrafine particulate matters in addition to regular exposure to traffic emission.

Assessing the relationship between surface levels of PM2.5 and PM10 particulate matter impact on COVID-19 in Milan, Italy

The novel coronavirus disease (COVID-19) is a highly pathogenic, transmittable and invasive pneumococcal disease caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), which emerged in December 2019 and January 2020 in Wuhan city, Hubei province, China and fast spread later on the middle of February 2020 in the Northern part of Italy and Europe. This study investigates the correlation between the degree of accelerated diffusion and lethality of COVID-19 and the surface air pollution in Milan metropolitan area, Lombardy region, Italy. Daily average concentrations of inhalable particulate matter (PM) in two size fractions PM2.5, PM10 and maxima PM10 ground level atmospheric pollutants together air quality and climate variables (daily average temperature, relative humidity, wind speed, atmospheric pressure field and Planetary Boundary Layer-PBL height) collected during 1 January-30 April 2020 were analyzed. In spite of being considered primarily transmitted by indoor bioaerosols droplets and infected surfaces, or direct human-to-human personal contacts, it seems that high levels of urban air pollution, weather and specific climate conditions have a significant impact on the increased rates of confirmed COVID-19 Total number, Daily New and Total Deaths cases, possible attributed not only to indoor but also to outdoor airborne bioaerosols distribution. Our analysis demonstrates the strong influence of daily averaged ground levels of particulate matter concentrations, positively associated with average surface air temperature and inversely related to air relative humidity on COVID-19 cases outbreak in Milan. Being a novel pandemic coronavirus (SARS-CoV-2) version, COVID-19 might be ongoing during summer conditions associated with higher temperatures and low humidity levels. Presently is not clear if this protein "spike" of the new coronavirus COVID-19 is involved through attachment mechanisms on indoor or outdoor airborne aerosols in the infectious agent transmission from a reservoir to a susceptible host in some agglomerated urban areas like Milan is.

Assessing the relationship between surface levels of PM2.5 and PM10 particulate matter impact on COVID-19 in Milan, Italy

The novel coronavirus disease (COVID-19) is a highly pathogenic, transmittable and invasive pneumococcal disease caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), which emerged in December 2019 and January 2020 in Wuhan city, Hubei province, China and fast spread later on the middle of February 2020 in the Northern part of Italy and Europe. This study investigates the correlation between the degree of accelerated diffusion and lethality of COVID-19 and the surface air pollution in Milan metropolitan area, Lombardy region, Italy. Daily average concentrations of inhalable particulate matter (PM) in two size fractions PM2.5, PM10 and maxima PM10 ground level atmospheric pollutants together air quality and climate variables (daily average temperature, relative humidity, wind speed, atmospheric pressure field and Planetary Boundary Layer-PBL height) collected during 1 January-30 April 2020 were analyzed. In spite of being considered primarily transmitted by indoor bioaerosols droplets and infected surfaces, or direct human-to-human personal contacts, it seems that high levels of urban air pollution, weather and specific climate conditions have a significant impact on the increased rates of confirmed COVID-19 Total number, Daily New and Total Deaths cases, possible attributed not only to indoor but also to outdoor airborne bioaerosols distribution. Our analysis demonstrates the strong influence of daily averaged ground levels of particulate matter concentrations, positively associated with average surface air temperature and inversely related to air relative humidity on COVID-19 cases outbreak in Milan. Being a novel pandemic coronavirus (SARS-CoV-2) version, COVID-19 might be ongoing during summer conditions associated with higher temperatures and low humidity levels. Presently is not clear if this protein "spike" of the new coronavirus COVID-19 is involved through attachment mechanisms on indoor or outdoor airborne aerosols in the infectious agent transmission from a reservoir to a susceptible host in some agglomerated urban areas like Milan is.

Spatiotemporal Patterns in River Water Quality and Pollution Source Apportionment in the Arid Beichuan River Basin of Northwestern China Using Positive Matrix Factorization Receptor Modeling Techniques

Deteriorating surface water quality has become an important environmental problem in China. In this study, river water quality was monitored in July (wet season) and October (dry season) 2019 at 26 sites, and a water quality index (WQI) and positive matrix factorization (PMF) model were used to assess surface water quality and identify pollution sources in the Beichuan River basin, Qinghai Province, China. The results showed that 53.85% and 76.92% of TN, 11.54% and 34.62% of TP, 65.38% and 76.92% of Fe, and 11.54% and 15.38% of Mn samples in the dry and wet seasons, respectively, exceeded the Chinese Government’s Grade III standards for surface water quality. The spatial variation in water quality showed that it gradually deteriorated from upstream to downstream as a result of human activity. The temporal variation showed that water quality was poorer in the wet season than in the dry season because of the rainfall runoff effect. The PMF model outputs showed that the primary sources of pollution in the wet season were mineral weathering and organic pollution sources, domestic and industrial sewage, and agricultural and urban non-point pollution sources. However, in the dry season, the primary sources were mineral weathering and organic pollution sources, industrial sewage, and domestic sewage. Our results suggest that the point pollution sources (domestic and industrial sewage) should be more strictly controlled, as a priority, in order to prevent the continued deterioration in water quality.

Organic chemical characterization of size segregated particulate matter samples collected from a thermal power plant area

Kütahya city, a thermal power plant (TPPs) affected region of Turkey, has serious air quality problems like similar industrial regions of the world due to the emissions from three closely-located coal-fired TPPs, residential coal combustion along with the contribution of several industrial stacks. The organic chemical speciation of ambient size-segregated particulate matter (PM) was investigated during two seasons at two sites with different pollution characteristics (urban and rural). The ambient PM was collected using a high volume cascade impactor, with 6 stages: PM_>10.2, PM_10.2-4.2, PM_4.2-2.1, PM_2.1-1.3, PM_1.3-0.69 and PM_<0.69. Collected PM samples were extracted with organic solvents and the organic composition (Polycyclic aromatic hydrocarbons (PAHs), n-alkanes and carboxylic acids) was determined by GC-MS. Sources of the organic species were assessed using molecular PAH diagnostic ratios, carbon preference index and wax percentages. More than 70% of the PM-bound PAHs were quantified in submicron particles. Similarly, 34-42% of n-alkanes and approximately 30% of the carboxylic acids were found on the smallest particles. The main sources of the PM-bound organic species were considered as the anthropogenic emissions such as coal and biomass combustion and also vehicular emissions rather than the biogenic sources. Considerably high cancer risk levels were obtained through inhalation of PAHs. Seasonal variations and size distributions of the carboxylic acids and levoglucosan were also evaluated. Polar organic compound concentrations were higher in the summer period at both locations probably due to the higher sunlight intensity and temperature favoring their photochemical formation.

Chemical Composition of PM10 in 16 Urban, Industrial and Background Sites in Italy

Italy is characterized by a very variable configuration in terms of altitude, proximity to the sea, latitude and the presence of industrial plants. This paper summarizes the chemical characterization of PM10 obtained from 38 sampling campaigns carried out in 16 sites in Italy during the years 2008–2018. Chemical determinations include all macro-components (six macro-elements, eight ions, elemental carbon and organic carbon). The sum of the individual components agrees well with the PM10 mass. The chemical composition of the atmospheric aerosol clearly reflects the variety in the Italian territory and the pronounced seasonal variations in the meteoclimatic conditions that characterize the country. Macro-sources reconstruction allowed us to identify and evaluate the strength of the main PM10 sources in different areas. On 10 sampling sites, the soluble and insoluble fractions of 23 minor and trace elements were also determined. Principal Component Analysis was applied to these data to highlight the relationship between the elemental composition of PM10 and the characteristics of the sampling sites.

Characterization and source identification of PM2.5-bound polycyclic aromatic hydrocarbons in urban, suburban, and rural ambient air, central China during summer harvest

Characterization and source identification of PM_2.5-bound polycyclic aromatic hydrocarbons (PAHs) are conducted in urban Wuhan (WH), suburban Pingdingshan (PDS), and rural Suizhou (SZ) in China during summer harvest. This study analyzes 16 priority PAHs with 38 PM._2.5 samples in June. PAHs had similar physical-chemical properties like polychlorinated biphenyls (PCBs) and organochlorine pesticides (OCPs), which had been listed as Priority Pollutants. The concentration and detection frequency of OCPs and PCBs were considerably lower than those of PAHs in PM_2.5. Results indicate that PDS adjoining the highway has the highest PM_2.5-bound PAHs. SZ possesses the lowest concentration of PAHs. Principal component analysis and multivariate linear regression model and molecular diagnostic ratio distinguish the sources. Vehicle emissions and coal combustion are extracted in three sites, while the source of PDS also includes gas combustion. SZ was affected by gas combustion and petroleum. The potential source contribution function and the concentration-weighted trajectory track the potential pollution area. The sampling places might be affected by the local sources and short distance transmission cannot be neglected. The incremental lifetime cancer risks (ILCRs) model evaluates the exposure risk of PAHs. According to the ILCR model, WH and PDS are exposed to harmful PAHs. By contrast, SZ is a substantially safe place.

Hybrid multiple-site mass closure and source apportionment of PM2.5 and aerosol acidity at major cities in the Po Valley

This study investigates the major chemical components, particle-bound water content, acidity (pH), and major potential sources of PM_2.5 in major cities (Belluno, Conegliano, Vicenza, Mestre, Padua, and Rovigo) in the eastern end of the Po Valley. The measured PM_2.5 mass was reconstructed using a multiple-site hybrid chemical mass closure approach that also accounts for aerosol inorganic water content (AWC) estimated by the ISORROPIA-II model. Annually, organic matter accounted for 31-45% of the PM_2.5 at all sites, followed by nitrate (10-19%), crustal material (10-14%), sulfate (8-10%), ammonium (5-9%), elemental carbon (4-7%), other inorganic ions (3-4%), and trace elements (0.2-0.3%). Water represented 7-10% of measured PM_2.5. The ambient aerosol pH varied from 1.5 to 4.5 with lower values in summer (average in all sites 2.2 ± 0.3) and higher in winter (3.9 ± 0.3). Six major PM_2.5 sources were quantitatively identified with multiple-site positive matrix factorization: secondary sulfate (34% of PM_2.5), secondary nitrate (30%), biomass burning (17%), traffic (11%), re-suspended dust (5%), and fossil fuel combustion (3%). Biomass burning accounted for ~90% of total PAHs. Inorganic aerosol acidity was driven primarily by secondary sulfate, fossil fuel combustion (decreasing pH), secondary nitrate, and biomass burning (increasing pH). Secondary nitrate was the primary driver of the inorganic AWC variability. A concentration-weighted trajectory (multiple-site) analysis was used to identify potential source areas for the various factors and modeled aerosol acidity. Eastern and Central Europe were the main source areas of secondary species. Less acidic aerosol was associated with air masses originating from Northern Europe owing to the elevated presence of the nitrate factor. More acidic particles were observed for air masses traversing the Po Valley and the Mediterranean, possibly due to the higher contributions of fossil fuel combustion factor and the loss of nitric acid due to its interaction with coarse sea-salt particles.

Formation of metal-organic ligand complexes affects solubility of metals in airborne particles at an urban site in the Po valley

Metals in atmospheric aerosols play potentially an important role in human health and ocean primary productivity. However, the lack of knowledge about solubility and speciation of metal ions in the particles or after solubilisation in aqueous media (sea or surface waters, cloud or rain droplets, biological fluids) limits our understanding of the underlying physico-chemical processes. In this work, a wide range of metals, their soluble fractions, and inorganic/organic compounds contained in urban particulate matter (PM) from Padua (Italy) were determined. Metal solubility tests have been performed by dissolving the PM in water and in solutions simulating rain droplet composition. The water-soluble fractions of the metal ions and of the organic compounds having ligand properties have been subjected to a multivariate statistical procedure, in order to elucidate associations among the aqueous concentrations of these PM components in simulated rain droplets. In parallel, a multi-dimensional speciation calculation has been performed to identify the stoichiometry and the amount of metal-ligand complexes theoretically expected in aqueous solutions. Both approaches showed that the solubility and the aqueous speciation of metal ions were differently affected by the presence of inorganic and organic ligands in the PM. The solubility of Al, Cr, and Fe was strongly correlated to the concentrations of oxalic acid, as their oxalate complexes represented the expected dominant species in aqueous solutions. Oxalates of Al represented ∼98% of soluble Al, while oxalates of Cu represented 34-75% of the soluble Cu, and oxalates of Fe represented 76% of soluble Fe. The oxidation state of Fe can strongly impact the speciation picture. If Fe is present as Fe(II) rather than Fe(III), the amount of Cr and Cu complexed with diacids can increase from 75% to 94%, and from 32% to 53%, respectively. For other metals, the solubility depended on the formation of soluble aquo-complexes, hence with a scarce effect of the organic ligands. An iron-oxalate complex was also directly detected in aerosol sample extracts.

PAHs in road dust of Nanjing Chemical Industry Park, China: chemical composition, sources, and risk assessment

The objectives of the present study were to research the chemical composition of PAHs in PM_2.5 of road dust, explore potential sources of PAHs, and assess their carcinogenic risk. Fifty-six road dust samples were collected on the arterial and sub-arterial roads at Nanjing Chemical Industry Park during the sampling periods of autumn and winter. A resuspension system in the laboratory was used to simulate the naturally suspended road dust in the environment and collect PM_2.5. Ace was not detected, but other PAHs were found. The pollution level of PAHs was 43.66 ± 15.79 mg kg^-1; and 4-ring PAHs, which accounted for 35.14% of PAHs, were the main pollutants. The contents of BeP and BghiP were the highest, accounting for 12.23% and 12.06% of PAHs, respectively. The PAHs concentrations were found to be higher in winter due to the physicochemical characteristics of PAHs and the meteorological conditions. The sequence of PAHs concentrations was: sub-arterial road in winter > arterial road in winter > arterial road in autumn > sub-arterial road in autumn. Traffic emission and industrial sources led to higher PAHs concentrations on the arterial roads in autumn. Meteorological conditions and road characteristics, such as vehicular speed, frequency of road dust sweeping, and road width, led to higher PAHs concentrations on the sub-arterial roads in winter. Principal component analysis (PCA) and the ratios of Ant/(Ant + Phe), Flu/(Flu + Pyr), InP/(InP + BP), and BaA/(BaA + Chr) were used to distinguish the PAHs sources, which indicated fossil fuel combustion, traffic emissions, and petroleum exhaust as the main PAHs sources. The application of the ILCR model for PAHs in road dust showed a higher carcinogenic risk for children and adults (6.01 × 10^-5 and 5.80 × 10^-5, respectively) on the sub-arterial roads in winter, indicating a high potential carcinogenic risk at the Chemical Industrial Park.

Carbonaceous Aerosols Collected at the Observatory of Monte Curcio in the Southern Mediterranean Basin

This work provides the first continuous measurements of carbonaceous aerosol at the Global Atmosphere Watch (GAW) Monte Curcio regional station, within the southern Mediterranean basin. We specifically analyzed elemental carbon (EC) and organic carbon (OC) concentrations in particulate matter (PM) samples, collected from April to December during the two years of 2016 and 2017. The purpose of the study is to understand the behavior of both PM and carbonaceous species, in their fine and coarse size fraction, along with their seasonal variability. Based on 18 months of observations, we obtained a dataset that resulted in a vast range of variability. We found the maximum values in summer, mainly related to the enhanced formation of secondary pollutants owing to intense solar radiation, also due to the high frequency of wildfires in the surrounding areas, as well as to the reduced precipitation and aerosol-wet removal. We otherwise observed the lowest levels during fall, coinciding with well-ventilated conditions, low photochemical activity, higher precipitation amounts, and less frequency of Saharan dust episodes. We employed the HYSPLIT model to identify long-range transport from Saharan desert. We found that the Saharan dust events caused higher concentrations of PM and OC in the coarser size fraction whereas the wildfire events likely influenced the highest PM, OC, and EC concentrations we recorded for the finer fraction.

Annual, seasonal, and morning rush hour Land Use Regression models for black carbon in a school catchment area of Milan, Italy

INTRODUCTION

The European Environment Agency has identified Northern Italy as one of the most polluted areas in Europe. Among air contaminants, black carbon (BC) has been identified as a sensitive marker of traffic related air pollution. This study aims to investigate the spatial distribution of BC in the catchment area of an elementary school of Milan, the biggest city in Northern Italy, using Land Use Regression (LUR) models and focusing especially on Morning Rush Hour (MRH).

METHODS

Two recruitment campaigns were performed asking schoolchildren's parents and residents of the study area to host a monitoring site in their own dwellings. Finally, 34 monitoring sites and 1 reference site were sampled. BC was measured in two seasonal campaigns using eight micro-aethalometers. Six seasonal and annual LUR models were developed, 3 focused on MRH.

RESULTS

Overall, median BC was 3247 and 1309 ng/m³ in the cold and warm season, respectively. In both seasons, there was a significant spatial variation between the monitoring sites. MRH values were higher than the daily values with median concentrations of 4227 and 2331 ng/m³, respectively. Developed LUR models showed that BC variability is well explained only by traffic variables; R² ranged from 0.52 to 0.79 and from 0.65 to 0.81, for seasonal/annual and MRH LUR models respectively.

DISCUSSION

LUR models based on traffic variables explain most of the measured BC distribution variability for both warm and cold season. MRH represents a critical moment for BC during all the year, with an increase of 1000 ng/m³ respective to the daily median value and differences in magnitude according to location. Our results highlight that the mobility issue is one of the most important challenges to reduce air pollution in the city of Milan and this is of particular concern for elementary schoolchildren that commute to school during MRH.

Ultrafine particles and PM2.5 in the air of cities around the world: Are they representative of each other?

Can mitigating only particle mass, as the existing air quality measures do, ultimately lead to reduction in ultrafine particles (UFP)? The aim of this study was to provide a broader urban perspective on the relationship between UFP, measured in terms of particle number concentration (PNC) and PM_2.5 (mass concentration of particles with aerodynamic diameter < 2.5 μm) and factors that influence their concentrations. Hourly average PNC and PM_2.5 were acquired from 10 cities located in North America, Europe, Asia, and Australia over a 12-month period. A pairwise comparison of the mean difference and the Kolmogorov-Smirnov test with the application of bootstrapping were performed for each city. Diurnal and seasonal trends were obtained using a generalized additive model (GAM). The particle number to mass concentration ratios and the Pearson's correlation coefficient were calculated to elucidate the nature of the relationship between these two metrics. Results show that the annual mean concentrations ranged from 8.0 × 10³ to 19.5 × 10³ particles·cm^-3 and from 7.0 to 65.8 μg·m^-3 for PNC and PM_2.5, respectively, with the data distributions generally skewed to the right, and with a wider spread for PNC. PNC showed a more distinct diurnal trend compared with PM_2.5, attributed to the high contributions of UFP from vehicular emissions to PNC. The variation in both PNC and PM_2.5 due to seasonality is linked to the cities' geographical location and features. Clustering the cities based on annual median concentrations of both PNC and PM_2.5 demonstrated that a high PNC level does not lead to a high PM_2.5, and vice versa. The particle number-to-mass ratio (in units of 10⁹ particles·μg^-1) ranged from 0.14 to 2.2, >1 for roadside sites and <1 for urban background sites with lower values for more polluted cities. The Pearson's r ranged from 0.09 to 0.64 for the log-transformed data, indicating generally poor linear correlation between PNC and PM_2.5. Therefore, PNC and PM_2.5 measurements are not representative of each other; and regulating PM_2.5 does little to reduce PNC. This highlights the need to establish regulatory approaches and control measures to address the impacts of elevated UFP concentrations, especially in urban areas, considering their potential health risks.

PM2.5 Pollution in Xingtai, China: Chemical Characteristics, Source Apportionment, and Emission Control Measures

Beijing-Tianjin-Hebei (BTH) and its surrounding areas are one of the most polluted regions in China. Xingtai, as a heavy industrial city of BTH and its surrounding areas, has been experiencing a severe PM2.5 pollution in recent years, characterized by extremely high concentrations of PM2.5. In 2014, PM2.5 mass concentrations monitored by online instruments in urban areas of Xingtai were 116, 77, 128, and 200 µg m−3 in spring, summer, autumn and winter, respectively, with annually average concentrations of 130 µg m−3 exhibiting 3.7 times higher than National Ambient Air Quality Standard (NAAQS) value for PM2.5 (35 µg m−3). To identify PM2.5 emission sources, ambient PM2.5 samples were collected during both cold and warm periods in 2014 in urban areas of Xingtai. Organic carbon (OC), sulfate, nitrate, ammonium and elemental carbon (EC) were the dominant components of PM2.5, accounting for 13%, 11%, 12%, 11% and 8% in the cold period, respectively, and 11%, 12%, 9%, 6%, and 5% in the warm period, respectively. Source apportionment results indicated that coal combustion (24.4%) was the largest PM2.5 emission source, followed by secondary sulfate (22.2%), secondary nitrate (18.4%), vehicle exhaust dust (12.4%), fugitive dust (9.7%), construction dust (5.5%), soil dust (3.4%) and metallurgy dust (1.6%). Based on PM2.5 source apportionment results, some emission control measures, such as replacing bulk coal with clean energy sources, controlling coal consumption by coal-fired boiler upgrades, halting operations of unlicensed small polluters, and controlling fugitive and VOCs emission, were proposed to be implemented in order to improve Xingtai’s ambient air quality.

Comparison of Measurement-Based Methodologies to Apportion Secondary Organic Carbon (SOC) in PM2.5: A Review of Recent Studies

Secondary organic aerosol (SOA) is known to account for a major fraction of airborne particulate matter, with significant impacts on air quality and climate at the global scale. Despite the substantial amount of research studies achieved during these last decades, the source apportionment of the SOA fraction remains difficult due to the complexity of the physicochemical processes involved. The selection and use of appropriate approaches are a major challenge for the atmospheric science community. Several methodologies are nowadays available to perform quantitative and/or predictive assessments of the SOA amount and composition. This review summarizes the current knowledge on the most commonly used approaches to evaluate secondary organic carbon (SOC) contents: elemental carbon (EC) tracer method, chemical mass balance (CMB), SOA tracer method, radiocarbon (14C) measurement and positive matrix factorization (PMF). The principles, limitations, challenges and good practices of each of these methodologies are discussed in the present article. Based on a comprehensive—although not exhaustive—review of research papers published during the last decade (2006–2016), SOC estimates obtained using these methodologies are also summarized for different regions across the world. Conclusions of some studies which are directly comparing the performances of different methodologies are then specifically discussed. An overall picture of SOC contributions and concentrations obtained worldwide for urban sites under similar conditions (i.e., geographical and seasonal ones) is also proposed here. Finally, further needs to improve SOC apportionment methodologies are also identified and discussed.

Chemically and size-resolved particulate matter dry deposition on stone and surrogate surfaces inside and outside the low emission zone of Milan: application of a newly developed "Deposition Box"

The collection of atmospheric particles on not-filtering substrates via dry deposition, and the subsequent study of the particle-induced material decay, is trivial due to the high number of variables simultaneously acting on the investigated surface. This work reports seasonally resolved data of chemical composition and size distribution of particulate matter deposed on stone and surrogate surfaces obtained using a new method, especially developed at this purpose. A "Deposition Box" was designed allowing the particulate matter dry deposition to occur selectively removing, at the same time, variables that can mask the effect of airborne particles on material decay. A pitched roof avoided rainfall and wind variability; a standardised gentle air exchange rate ensured a continuous "sampling" of ambient air leaving unchanged the sampled particle size distribution and, at the same time, leaving quite calm condition inside the box, allowing the deposition to occur. Thus, the "Deposition Box" represents an affordable tool that can be used complementary to traditional exposure systems. With this system, several exposure campaigns, involving investigated stone materials (ISMs) (Carrara Marble, Botticino limestone, Noto calcarenite and Granite) and surrogate (Quartz, PTFE, and Aluminium) substrates, have been performed in two different sites placed in Milan (Italy) inside and outside the low emission zone. Deposition rates (30-90 μg cm^-2 month^-1) showed significant differences between sites and seasons, becoming less evident considering long-period exposures due to a positive feedback on the deposition induced by the deposited particles. Similarly, different stone substrates influenced the deposition rates too. The collected deposits have been observed with optical and scanning electron microscopes and analysed by ion chromatography. Ion deposition rates were similar in the two sites during winter, whereas it was greater outside the low emission zone during summer and considering the long-period exposure. The dimensional distribution of the collected deposits showed a significant presence of fine particles in agreement with deposition rate of the ionic fraction. The obtained results allowed to point out the role of the fine particles fraction and the importance of making seasonal studies.

Wood burning pollution in southern Chile: PM2.5 source apportionment using CMB and molecular markers

Temuco is a mid-size city representative of severe wood smoke pollution in southern Chile; i.e., ambient 24-h PM_2.5 concentrations have exceeded 150 μg/m³ in the winter season and the top concentration reached 372 μg/m³ in 2010. Annual mean concentrations have decreased but are still above 30 μg/m³. For the very first time, a molecular marker source apportionment of ambient organic carbon (OC) and PM_2.5 was conducted in Temuco. Primary resolved sources for PM_2.5 were wood smoke (37.5%), coal combustion (4.4%), diesel vehicles (3.3%), dust (2.2%) and vegetative detritus (0.7%). Secondary inorganic PM_2.5 (sulfates, nitrates and ammonium) contributed 4.8% and unresolved organic aerosols (generated from volatile emissions from incomplete wood combustion), including secondary organic aerosols, contributed 47.1%. Adding the contributions of unresolved organic aerosols to those from primary wood smoke implies that wood burning is responsible for 84.6% of the ambient PM_2.5 in Temuco. This predominance of wood smoke is ultimately due to widespread poverty and a lack of efficient household heating methods. The government has been implementing emission abatement policies but achieving compliance with ambient air quality standards for PM_2.5 in southern Chile remains a challenge.

Source apportionment and a novel approach of estimating regional contributions to ambient PM2.5 in Haikou, China

A novel approach was developed to estimate regional contributions to ambient PM_2.5 in Haikou, China. In this paper, the investigation was divided into two main steps. The first step: analysing the characteristics of the chemical compositions of ambient PM_2.5, as well as the source profiles, and then conducting source apportionments by using the CMB and CMB-Iteration models. The second step: the development of estimation approaches for regional contributions in terms of local features of Haikou and the results of source apportionment, and estimating regional contributions to ambient PM_2.5 in Haikou by this new approach. The results indicate that secondary sulphate, resuspended dust and vehicle exhaust were the major sources of ambient PM_2.5 in Haikou, contributing 9.9-21.4%, 10.1-19.0% and 10.5-20.2%, respectively. Regional contributions to ambient PM_2.5 in Haikou in spring, autumn and winter were 22.5%, 11.6% and 32.5%, respectively. The regional contribution in summer was assumed to be zero according to the better atmospheric quality and assumptions of this new estimation approach. The higher regional contribution in winter might be mainly attributable to the transport of polluted air originating in mainland China, especially from the north, where coal is burned for heating in winter.

Estimation of local and external contributions of biomass burning to PM2.5 in an industrial zone included in a large urban settlement

A total of 85 PM_2.5 samples were collected at a site located in a large industrial zone (Porto Marghera, Venice, Italy) during a 1-year-long sampling campaign. Samples were analyzed to determine water-soluble inorganic ions, elemental and organic carbon, and levoglucosan, and results were processed to investigate the seasonal patterns, the relationship between the analyzed species, and the most probable sources by using a set of tools, including (i) conditional probability function (CPF), (ii) conditional bivariate probability function (CBPF), (iii) concentration weighted trajectory (CWT), and (iv) potential source contribution function (PSCF) analyses. Furthermore, the importance of biomass combustions to PM_2.5 was also estimated. Average PM_2.5 concentrations ranged between 54 and 16 μg m^-3 in the cold and warm period, respectively. The mean value of total ions was 11 μg m^-3 (range 1-46 μg m^-3): The most abundant ion was nitrate with a share of 44 % followed by sulfate (29 %), ammonium (14 %), potassium (4 %), and chloride (4 %). Levoglucosan accounted for 1.2 % of the PM_2.5 mass, and its concentration ranged from few ng m^-3 in warm periods to 2.66 μg m^-3 during winter. Average concentrations of levoglucosan during the cold period were higher than those found in other European urban sites. This result may indicate a great influence of biomass combustions on particulate matter pollution. Elemental and organic carbon (EC, OC) showed similar behavior, with the highest contributions during cold periods and lower during summer. The ratios between biomass burning indicators (K⁺, Cl^-, NO₃^-, SO₄^2-, levoglucosan, EC, and OC) were used as proxy for the biomass burning estimation, and the contribution to the OC and PM_2.5 was also calculated by using the levoglucosan (LG)/OC and LG/PM_2.5 ratios and was estimated to be 29 and 18 %, respectively.

Nitration of pollen aeroallergens by nitrate ion in conditions simulating the liquid water phase of atmospheric particles

Pollen aeroallergens are present in atmospheric particulate matter (PM) where they can be found in coarse biological particles such as pollen grains (aerodynamic diameter d_ae>10μm), as well as fragments in the finest respirable particles (PM2.5; d_ae<2.5μm). Nitration of tyrosine residues in pollen allergenic proteins can occur in polluted air, and inhalation and deposition of these nitrated proteins in the human respiratory tract may lead to adverse health effects by enhancing the allergic response in population. Previous studies investigated protein nitration by atmospheric gaseous pollutants such as nitrogen dioxide and ozone. In this work we report, for the first time, a study on protein nitration by nitrate ion in aqueous solution, at nitrate concentrations and pH conditions simulating those occurring in the atmospheric aerosol liquid water phase. Experiments have been carried out on the Bovine serum albumin (BSA) protein and the recombinant Phleum pratense allergen (Phl p 2) both in the dark and under UV-A irradiation (range 4-90Wm^-2) to take into account thermal and/or photochemical nitration processes. For the latter protein, modifications in the allergic response after treatment with nitrate solutions have been evaluated by immunoblot analyses using sera from grass-allergic patients. Experimental results in bulk solutions showed that protein nitration in the dark occurs only in dilute nitrate solutions and under very acidic conditions (pH<3 for BSA; pH<2.2 for Phl p 2), while nitration is always observed (at pH0.5-5) under UV-A irradiation, both in dilute and concentrated nitrate solutions, being significantly enhanced at the lowest pH values. In some cases, protein nitration resulted in an increase of the allergic response.

Five primary sources of organic aerosols in the urban atmosphere of Belgrade (Serbia)

Biomass burning and primary biological aerosol particles (PBAPs) represent important primary sources of organic compounds in the atmosphere. These particles and compounds are able to affect climate and human health. In the present work, using HPLC-orbitrapMS, we determined the atmospheric concentrations of molecular markers such as anhydrosugars and phenolic compounds that are specific for biomass burning, as well as the concentrations of sugars, alcohol sugars and d- and l-amino acids (D-AAs and L-AAs) for studying PBAPs in Belgrade (Serbia) aerosols collected in September-December 2008. In these samples, high levels of all these biomarkers were observed in October. Relative percentages of vanillic (V), syringic compounds (S) and p-coumaric acid (PA), as well as levoglucosan/mannosan (L/M) ratios, helped us discriminate between open fire events and wood combustion for domestic heating during the winter. L-AAs and D-AAs (1% of the total) were observed in Belgrade aerosols mainly in September-October. During open fire events, mean D-AA/L-AA (D/L) ratio values of aspartic acid, threonine, phenylalanine, alanine were significantly higher than mean D/L values of samples unaffected by open fire. High levels of AAs were observed for open biomass burning events. Thanks to four different statistical approaches, we demonstrated that Belgrade aerosols are affected by five sources: a natural source, a source related to fungi spores and degraded material and three other sources linked to biomass burning: biomass combustion in open fields, the combustion of grass and agricultural waste and the combustion of biomass in stoves and industrial plants. The approach employed in this work, involving the determination of specific organic tracers and statistical analysis, proved useful to discriminate among different types of biomass burning events.

Particulate-bound polycyclic aromatic hydrocarbon sources and determinants in residential homes

Human exposure to polycyclic aromatic hydrocarbons (PAHs) in indoor environments can be particularly relevant because people spend most of their time inside buildings, especially in homes. This study aimed to investigate the most important particle-bound PAH sources and exposure determinants in PM_2.5 samples collected in 19 homes located in northern Italy. Complementary information about ion content in PM₁₀ was also collected in 12 of these homes. Three methods were used for the identification of PAH sources and determinants: diagnostic ratios with principal component and hierarchical cluster analyses (PCA and HCA), chemical mass balance (CMB) and linear mixed models (LMMs). This combined and tiered approach allowed the infiltration of outdoor PAHs into indoor environments to be identified as the most important source in winter, with a relevant role played by biomass burning and traffic exhausts to be identified as a general source of PAHs in both seasons. Tobacco smoke exhibited an important impact on PAH levels in smokers' homes, whereas in the whole sample, cooking food and natural gas sources played a minor or negligible role. Nitrate, sulfate and ammonium were the main inorganic constituents of indoor PM₁₀ owing to the secondary formation of ammonium sulfates and nitrates.

Characteristics and major sources of carbonaceous aerosols in PM2.5 in Emilia Romagna Region (Northern Italy) from four-year observations

The concentrations of organic and elemental carbon in PM2.5 aerosol samples were measured in two sites of Emilia Romagna (Po Valley, Northern Italy) in eight campaigns during different seasons from 2011 to 2014. Strong seasonality was observed with the highest OC concentrations during the cold periods (≈ 5.5 μg m(-3)) and the lowest in the warm months (≈ 2.7 μg m(-3)) as well as with higher EC levels in fall/winter (≈ 1.4 μg m(-3)) in comparison with spring/summer (≈ 0.6 μg m(-3)). Concerning spatial variability, there were no statistically significant difference (p<0.05) between OC concentrations at the two sampling sites in each campaign, while the EC values were nearly twofold higher levels at the urban site than those at the rural one. Specific molecular markers were investigated to attempt the basic apportionment of OC by discriminating between the main emission sources of primary OC, such as fossil fuels burning - including traffic vehicle emission - residential wood burning, and bio-aerosol released from plants and microorganisms, and the atmospheric photo-oxidation processes generating OCsec. The investigated markers were low-molecular-weight carboxylic acids - to describe the contribution of secondary organic aerosol - anhydrosugars - to quantify primary emissions from biomass burning - bio-sugars - to qualitatively estimate biogenic sources - and Polycyclic Aromatic Hydrocarbons - to differentiate among different combustion emissions. Using the levoglucosan tracer method, contribution of wood smoke to atmospheric OC concentration was computed. Wood burning accounts for 33% of OC in fall/winter and for 3% in spring/summer. A clear seasonal trend is also observed for the impact of secondary processes with higher contribution in the warm seasons (≈ 63%) in comparison with that in colder months (≈ 33%), that is consistent with enhanced solar radiation in spring/summer.

Review of receptor modeling methods for source apportionment

Efforts have been made to relate measured concentrations of airborne constituents to their origins for more than 50 years. During this time interval, there have been developments in the measurement technology to gather highly time-resolved, detailed chemical compositional data. Similarly, the improvements in computers have permitted a parallel development of data analysis tools that permit the extraction of information from these data. There is now a substantial capability to provide useful insights into the sources of pollutants and their atmospheric processing that can help inform air quality management options. Efforts have been made to combine receptor and chemical transport models to provide improved apportionments. Tools are available to utilize limited numbers of known profiles with the ambient data to obtain more accurate apportionments for targeted sources. In addition, tools are in place to allow more advanced models to be fitted to the data based on conceptual models of the nature of the sources and the sampling/analytical approach. Each of the approaches has its strengths and weaknesses. However, the field as a whole suffers from a lack of measurements of source emission compositions. There has not been an active effort to develop source profiles for stationary sources for a long time, and with many significant sources built in developing countries, the lack of local profiles is a serious problem in effective source apportionment. The field is now relatively mature in terms of its methods and its ability to adapt to new measurement technologies, so that we can be assured of a high likelihood of extracting the maximal information from the collected data.

IMPLICATIONS

Efforts have been made over the past 50 years to use air quality data to estimate the influence of air pollution sources. These methods are now relatively mature and many are readily accessible through publically available software. This review examines the development of receptor models and the current state of the art in extracting source identification and apportionments from ambient air quality data.

Levoglucosan and phenols in Antarctic marine, coastal and plateau aerosols

Due to its isolated location, Antarctica is a natural laboratory for studying atmospheric aerosols and pollution in remote areas. Here, we determined levoglucosan and phenolic compounds (PCs) at diverse Antarctic sites: on the plateau, a coastal station and during an oceanographic cruise. Levoglucosan and PCs reached the Antarctic plateau where they were observed in accumulation mode aerosols (with median levoglucosan concentrations of 6.4 pg m(-3) and 4.1 pg m(-3), and median PC concentrations of 15.0 pg m(-3) and 7.3 pg m(-3)). Aged aerosols arrived at the coastal site through katabatic circulation with the majority of the levoglucosan mass distributed on larger particulates (24.8 pg m(-3)), while PCs were present in fine particles (34.0 pg m(-3)). The low levoglucosan/PC ratios in Antarctic aerosols suggest that biomass burning aerosols only had regional, rather than local, sources. General acid/aldehyde ratios were lower at the coastal site than on the plateau. Levoglucosan and PCs determined during the oceanographic cruise were 37.6 pg m(-3) and 58.5 pg m(-3) respectively. Unlike levoglucosan, which can only be produced by biomass burning, PCs have both biomass burning and other sources. Our comparisons of these two types of compounds across a range of Antarctic marine, coastal, and plateau sites demonstrate that local marine sources dominate Antarctic PC concentrations.

Polycyclic aromatic hydrocarbons (PAHs) at traffic and urban background sites of northern Greece: source apportionment of ambient PAH levels and PAH-induced lung cancer risk

Thirteen particle-phase PAHs, including nine >4-ring congeners [Benz[a]anthracene (BaAn), Chrysene (Chry), Benzo[b]fluoranthene (BbF), Benzo[k]fluoranthene (BkF), Benzo[e]pyrene (BeP), Benzo[a]pyrene (BaP), Dibenzo[a,h]anthracene (dBaAn), Benzo[g,h,i]perylene (BghiPe), Indeno(1,2,3-c,d)pyrene (IP)], listed by IARC (International Agency for Research on Cancer) as class 1, class 2A, and 2B carcinogens, plus four ≤ 4-ring congeners [Phenanthrene (Ph), Anthracene (An), Fluoranthene (Fl), Pyrene (Py)], were concurrently measured in inhalable and respirable particle fractions (PM10 and PM2.5) at a heavy-traffic and an urban background site in Thessaloniki, northern Greece, during the warm and the cold period of the year. Carcinogenic and mutagenic potencies of the PAH-bearing particles were calculated, and the inhalation cancer risk (ICR) for local population was estimated. Finally, Chemical Mass Balance (CMB) modeling was employed for the source apportionment of ambient PAH levels and the estimated lung cancer risk. Resulted inhalation cancer risk during winter was found to be equivalent in the city center and the urban background area suggesting that residential wood burning may offset the benefits from minor traffic emissions.

Do green spaces affect the spatiotemporal changes of PM2.5 in Nanjing?

INTRODUCTION

Among the most dangerous pollutants is PM_2.5, which can directly pass through human lungs and move into the blood system. The use of nature-based solutions, such as increased vegetation cover in an urban landscape, is one of the possible solutions for reducing PM_2.5 concentration. Our study objective was to understand the importance of green spaces in pollution reduction.

METHODS

Daily PM_2.5 concentrations were manually collected at nine monitoring stations in Nanjing over a 534-day period from the air quality report of the China National Environmental Monitoring Center (CNEMC) to quantify the spatiotemporal change of PM_2.5 concentration and its empirical relationship with vegetation and landscape structure in Nanjing.

RESULTS

The daily average, minimum, and maximum PM_2.5 concentrations from the nine stations were 74.0, 14.2, and 332.0 μg m^-3, respectively. Out of the 534 days, the days recorded as "excellent" and "good" conditions were found mostly in the spring (30.7 %), autumn (25.6 %), and summer (24.5 %), with only 19.2 % of the days in the winter. High PM_2.5 concentrations exceeding the safe standards of the CNEMC were recorded predominately during the winter (39.3-100.0 %). Our hypothesis that green vegetation had the potential to reduce PM_2.5 concentration was accepted at specific seasons and scales. The PM_2.5 concentration appeared very highly correlated (R² > 0.85) with green cover in spring at 1-2 km scales, highly correlated (R² > 0.6) in autumn and winter at 4 km scale, and moderately correlated in summer (R² > 0.4) at 2-, 5-, and 6-km scales. However, a non-significant correlation between green cover and PM_2.5 concentration was found when its level was >75 μg m^-3. Across the Nanjing urban landscape, the east and southwest parts had high pollution levels.

CONCLUSIONS

Although the empirical models seemed significant for spring only, one should not devalue the importance of green vegetation in other seasons because the regulations are often complicated by vegetation, meteorological conditions, and human activities.