Performance Assessment of Two Low-Cost PM2.5 and PM10 Monitoring Networks in the Padana Plain (Italy).

Two low-cost (LC) monitoring networks, PurpleAir (instrumented by Plantower PMS5003 sensors) and AirQino (Novasense SDS011), were assessed in monitoring PM2.5 and PM10 daily concentrations in the Padana Plain (Northern Italy). A total of 19 LC stations for PM2.5 and 20 for PM10 concentrations were compared vs. regulatory-grade stations during a full "heating season" (15 October 2022-15 April 2023). Both LC sensor networks showed higher accuracy in fitting the magnitude of PM10 than PM2.5 reference observations, while lower accuracy was shown in terms of RMSE, MAE and R2. AirQino stations under-estimated both PM2.5 and PM10 reference concentrations (MB = -4.8 and -2.9 µg/m3, respectively), while PurpleAir stations over-estimated PM2.5 concentrations (MB = +5.4 µg/m3) and slightly under-estimated PM10 concentrations (MB = -0.4 µg/m3). PurpleAir stations were finer than AirQino at capturing the time variation of both PM2.5 and PM10 daily concentrations (R2 = 0.68-0.75 vs. 0.59-0.61). LC sensors from both monitoring networks failed to capture the magnitude and dynamics of the PM2.5/PM10 ratio, confirming their well-known issues in correctly discriminating the size of individual particles. These findings suggest the need for further efforts in the implementation of mass conversion algorithms within LC units to improve the tuning of PM2.5 vs. PM10 outputs.

Early-life exposure to ambient air pollution with cardiovascular risk factors in adolescents: Findings from the "Children of 1997" Hong Kong birth cohort.

BACKGROUND: Long-term exposure to ambient air pollution is associated with cardiovascular disease (CVD) risk. Little is known about the impact of early-life exposure to air pollutants on CVD risk factors in late adolescence, which may track into adulthood. To clarify, we examined this question in a unique setting with high air pollution and a high level of economic development. METHODS: This study leveraged the "Children of 1997" Hong Kong birth cohort (N = 8327), including here 3350 participants. We estimated ambient air pollutant exposure including inhalable particulate matter (PM10), sulfur dioxide (SO2), nitrogen dioxide (NO2) and nitrogen monoxide (NO) by growth phase (in utero, infancy, childhood) and overall based on residential address. Generalized linear regression was used to assess the associations of air pollutants exposure by growth phase and sex with CVD risk factors (fasting blood glucose, glycosylated hemoglobin, lipid profile, blood pressure, and body mass index) at 17.6 years. We also assessed whether associations varied by sex. RESULTS: Early life exposed had little association with glucose metabolism, blood pressure or body mass index, but after considering multiple comparisons early exposure to PM10 was associated with low density lipoprotein (LDL) in boys, with ß and 95 % confidence intervals (95 % CI) of 0.184 (0.069 to 0.298) mmol/l, 0.151 (0.056 to 0.248) mmol/l, and 0.157 (0.063 to 0.252) mmol/l by per interquartile range (IQR) increment of PM10 for in utero, infancy, and overall, respectively. No such associations were evident for girls, differences by sex were evident. CONCLUSIONS: Our study suggested sex-specific associations of early-life PM10 exposure with elevated LDL in adolescence, especially exposure in utero and infancy.

A comparative assessment of air pollutants of smog in wagah border and other sites in Lahore, Pakistan

Abstract Smog has become the fifth season of Pakistan especially in Lahore city. Increased level of air pollutants (primary and secondary) are thought to be responsible for the formation of smog in Lahore. Therefore, the current study was carried out for the evaluation of air pollutants (primary and secondary) of smog in Wagah border particularly and other sites (Jail road, Gulburg) Lahore. For this purpose, baseline data on winter smog from March to December on primary and secondary air pollutants and meteorological parameters was collected from Environmental Protection Department and Pakistan Meteorological Department respectively. Devices being used in both departments for analysis of parameters were also studied. Collected data was further statistically analyzed to determine the correlation of parameters with meteorological conditions and was subjected to air quality index. According to results, PM 10 and PM 2.5 were found very high above the NEQS. NOx concentrations were also high above the permissible limits whereas SO2 and O3 were found below the NEQS thus have no roles in smog formation. Air Quality Index (AQI) of pollutants was PM 2.5(86-227), PM 10 (46-332), NOx (26-110), O3 (19-84) and SO2 (10-95). AQI of PM 2.5 remained between moderate to very unhealthy levels. AQI of PM 10 remained between good to hazardous levels. AQI of NOx remained between good to unhealthy for sensitive groups levels. AQI of O3 and SO2 remained between good to moderate levels. Pearson correlation showed that every pollutant has a different relation with different or same parameters in different areas. It is concluded from the present study that particulate matter was much more responsible for smog formation. Although NOx also played role in smog formation. So there is need to reduce sources of particulate matter and NOx specifically in order to reduce smog formation in Lahore.

Resumo Smog tornou-se a quinta estação do Paquistão, especialmente na cidade de Lahore. Acredita-se que o aumento do nível de poluentes atmosféricos (primários e secundários) seja responsável pela formação de poluição atmosférica em Lahore. Portanto, o presente estudo foi realizado para a avaliação dos poluentes atmosféricos (primários e secundários) do smog na fronteira de Wagah em particular e em outros locais (Jail road, Gulburg) Lahore. Para este propósito, os dados de referência sobre a poluição atmosférica de inverno de março a dezembro sobre poluentes atmosféricos primários e secundários e parâmetros meteorológicos foram coletados do Departamento de Proteção Ambiental e do Departamento Meteorológico do Paquistão, respectivamente. Dispositivos sendo usados em ambos os departamentos para análise de parâmetros também foram estudados. Os dados coletados foram posteriormente analisados estatisticamente para determinar a correlação dos parâmetros com as condições meteorológicas e foram submetidos ao índice de qualidade do ar. De acordo com os resultados, PM 10 e PM 2,5 foram encontrados muito acima do NEQS. As concentrações de NOx também estavam muito acima dos limites permitidos, enquanto SO2 e O3 foram encontrados abaixo do NEQS, portanto, não têm papéis na formação de smog. O índice de qualidade do ar (AQI) de poluentes foi PM 2,5 (86-227), PM 10 (46-332), NOx (26-110), O3 (19-84) e SO2 (10-95). O AQI de PM 2,5 permaneceu entre níveis moderados a muito prejudiciais à saúde. O AQI de PM 10 permaneceu entre níveis bons e perigosos. AQI de NOx permaneceu entre bom e não saudável para os níveis de grupos sensíveis. O AQI de O3 e SO2 permaneceu entre níveis bons a moderados. A correlação de Pearson mostrou que cada poluente tem uma relação diferente com parâmetros diferentes ou iguais em áreas diferentes. Conclui-se do presente estudo que o material particulado foi muito mais responsável pela formação de smog. Embora o NOx também tenha desempenhado um papel na formação do smog. Portanto, é necessário reduzir as fontes de partículas e NOx, especificamente para reduzir a formação de smog em Lahore.

A comparative assessment of air pollutants of smog in wagah border and other sites in Lahore, Pakistan / Uma avaliação comparativa de poluentes do ar do smog na fronteira de wagah e outros sites em Lahore, Paquistão

Abstract Smog has become the fifth season of Pakistan especially in Lahore city. Increased level of air pollutants (primary and secondary) are thought to be responsible for the formation of smog in Lahore. Therefore, the current study was carried out for the evaluation of air pollutants (primary and secondary) of smog in Wagah border particularly and other sites (Jail road, Gulburg) Lahore. For this purpose, baseline data on winter smog from March to December on primary and secondary air pollutants and meteorological parameters was collected from Environmental Protection Department and Pakistan Meteorological Department respectively. Devices being used in both departments for analysis of parameters were also studied. Collected data was further statistically analyzed to determine the correlation of parameters with meteorological conditions and was subjected to air quality index. According to results, PM 10 and PM 2.5 were found very high above the NEQS. NOx concentrations were also high above the permissible limits whereas SO2 and O3 were found below the NEQS thus have no roles in smog formation. Air Quality Index (AQI) of pollutants was PM 2.5(86-227), PM 10 (46-332), NOx (26-110), O3 (19-84) and SO2 (10-95). AQI of PM 2.5 remained between moderate to very unhealthy levels. AQI of PM 10 remained between good to hazardous levels. AQI of NOx remained between good to unhealthy for sensitive groups' levels. AQI of O3 and SO2 remained between good to moderate levels. Pearson correlation showed that every pollutant has a different relation with different or same parameters in different areas. It is concluded from the present study that particulate matter was much more responsible for smog formation. Although NOx also played role in smog formation. So there is need to reduce sources of particulate matter and NOx specifically in order to reduce smog formation in Lahore.

Resumo Smog tornou-se a quinta estação do Paquistão, especialmente na cidade de Lahore. Acredita-se que o aumento do nível de poluentes atmosféricos (primários e secundários) seja responsável pela formação de poluição atmosférica em Lahore. Portanto, o presente estudo foi realizado para a avaliação dos poluentes atmosféricos (primários e secundários) do smog na fronteira de Wagah em particular e em outros locais (Jail road, Gulburg) Lahore. Para este propósito, os dados de referência sobre a poluição atmosférica de inverno de março a dezembro sobre poluentes atmosféricos primários e secundários e parâmetros meteorológicos foram coletados do Departamento de Proteção Ambiental e do Departamento Meteorológico do Paquistão, respectivamente. Dispositivos sendo usados ​​em ambos os departamentos para análise de parâmetros também foram estudados. Os dados coletados foram posteriormente analisados ​​estatisticamente para determinar a correlação dos parâmetros com as condições meteorológicas e foram submetidos ao índice de qualidade do ar. De acordo com os resultados, PM 10 e PM 2,5 foram encontrados muito acima do NEQS. As concentrações de NOx também estavam muito acima dos limites permitidos, enquanto SO2 e O3 foram encontrados abaixo do NEQS, portanto, não têm papéis na formação de smog. O índice de qualidade do ar (AQI) de poluentes foi PM 2,5 (86-227), PM 10 (46-332), NOx (26-110), O3 (19-84) e SO2 (10-95). O AQI de PM 2,5 permaneceu entre níveis moderados a muito prejudiciais à saúde. O AQI de PM 10 permaneceu entre níveis bons e perigosos. AQI de NOx permaneceu entre bom e não saudável para os níveis de grupos sensíveis. O AQI de O3 e SO2 permaneceu entre níveis bons a moderados. A correlação de Pearson mostrou que cada poluente tem uma relação diferente com parâmetros diferentes ou iguais em áreas diferentes. Conclui-se do presente estudo que o material particulado foi muito mais responsável pela formação de smog. Embora o NOx também tenha desempenhado um papel na formação do smog. Portanto, é necessário reduzir as fontes de partículas e NOx, especificamente para reduzir a formação de smog em Lahore.

Using a time-varying LCAR-based strategy to investigate the spatiotemporal pattern of association between short-term exposure to particulate matter and COVID-19 incidence: a case study in the continental USA.

Numerous studies have demonstrated that short-term exposure to particulate matter less than 10 µm (PM10) is positively associated with the COVID-19 incidence. However, no study has investigated the spatiotemporal pattern in this association, which plays important roles in identifying high-susceptibility regions and stages of epidemic. In this work, taking the 49 native states in America as an example, we used an advanced strategy to investigate this issue. First, time-series generalized additive model (GAM) were independently constructed to obtain the state-specific associations between short-term exposure to PM10 and the daily COVID-19 cases from 1 April 2020 to 31 December 2021. Then, a Leroux-prior-based conditional autoregression (LCAR) was used to spatially smoothen the associations. Third, the temporal variation of association and the reasons underlying the spatiotemporal heterogeneity were investigated by incorporating the time-varying GAM into LCAR. Results showed that PM10 was adversely associated with COVID-19 incidence in all the states. On average, a 10 µg/m3 increase of PM10 was associated with a 7.38% (95% CI 5.20-9.64%) increase in COVID-19 cases. A substantial spatial heterogeneity was observed, with strong associations in the middle and northeastern regions and weak associations in the western regions. The temporal trend of association presented a U shape, with the strongest association in the end of 2021. The vaccination rate was examined as a significant effect modifier. Our study provided the first evidence about the spatiotemporal pattern in PM10-COVID-19 associations and suggested that air pollution deserves more attention in the post-pandemic era and in the middle and northeastern regions in America for COVID-19 control and prevention.

Health risk assessment of aerosol particles (PM2.5 and PM10) during winter crop at the agricultural site of Delhi, India.

For the last few decades, air pollution in developing country like India is increasing, and it is a matter of huge concern due to its associated human health impacts. In this region, the burgeoning population, escalating urbanization and industrialization, has been cited as the major reason for such a high air pollution. The present study was carried out for health risk assessment of aerosol particles (PM10 and PM2.5) and its associated heavy metals of an agriculture farm site at Indian Agricultural Research Institute (IARI) considered to be green urban area in Delhi, India. The concentrations of both PM10 and PM2.5 varied significantly from 136 to 177 µg/m3 and 56 to 162 µg/m3, respectively at the site. In the present case, the highest PM10 and PM2.5 levels were reported in January, followed by December. The levels of ambient PM10 and PM2.5 are influenced by wind prevailing meteorology. These levels of PM10 and PM2.5 are more than the permissible limits of WHO guidelines of 15 and 5 µg/m3, respectively, thereby leading to high aerosol loadings specifically in winters. The PM concentration of the atmosphere was found to be negatively correlated with temperature during the sampling period. The concentrations of surface ozone O3 and NOx in the present study were observed to be high in February and March, respectively. The increasing air pollution in the city of Delhi poses a great risk to the human health, as the particulate matter loaded with heavy metals can enter humans via different pathways, viz., ingestion, inhalation, and absorption through skin. The mean hazard index for metals (Zn, Pb, Cd, As, Cr, and Ni) was observed within the acceptable limit (HI < 1), thereby indicating negligible non-carcinogenic effects to residing population. The carcinogenic risk assessment was conducted for Cd, Pb, and As only, as the concentrations for other metals were found to be quite low. The carcinogenic risk values were also within the limits of USEPA standards, indicating no carcinogenic risks to the health of children and adults residing near the site. This information about the PM pollution at the agricultural site and health risk assessment will serve as a baseline data in assessment of human health impacts due to air pollution at the local scale and can be used for development of mitigation strategies for tackling air pollution.

Assessment of meteorological parameters on air pollution variability over Delhi.

In this study, the relationships between meteorological parameters (relative humidity, wind speed, temperature, planetary boundary layer, and rainfall) and air pollutants (particulate matter and gaseous pollutants) have been evaluated during a 3-year period from 2019 to 2021. Diffusion and dispersion of air contaminants were significantly influenced by meteorology over the capital city. The results of correlation matrix and principal component analysis (PCA) suggest a season's specific influence of meteorological parameters on atmospheric pollutants' concentration. Temperature has the strongest negative impact on pollutants' concentration, and all the other studied meteorological parameters negatively (reduced) as well as positively (increased) impacted the air pollutants' concentration. A two-way process was involved during the interaction of pollutants with relative humidity and wind speed. Due to enhanced moisture-holding capacity during non-monsoon summers, particles get larger and settle down on the ground via dry deposition processes. Winter's decreased moisture-holding capacity causes water vapour coupled with air contaminants to remain suspended and further deteriorate the quality of the air. High wind speed helps in the dispersion and dilution but a high wind speed associated with dust particles may increase the pollutants' level downwind side. The PM2.5/PM10 variation revealed that the accumulation effect of relative humidity on PM2.5 was more intense than PM10. Daily average location-specific rainfall data revealed that moderate to high rainfall has a potential wet scavenging impact on both particulate matters and gaseous pollutants.

Aerial monitoring of atmospheric particulate matter produced by open-pit mining using low-cost airborne sensors.

Mining is an economic activity that entails the production and displacement of significant amounts of atmospheric particulate matter (PM) during operations involving intense earthcrushing or earthmoving. As high concentrations of PM may have adverse effects on human health, it is necessary to monitor and control the fugitive emissions of this pollutant. This paper presents an innovative methodology for the online monitoring of PM10 concentrations in air using a low-cost sensor (LCS, <300 USD) onboard an unmanned aerial vehicle. After comprehensive calibration, the LCS was horizontally flown over seven different areas of the large Riotinto copper mine (Huelva, Spain) at different heights to study the PM10 distribution at different longitudes and altitudes. The flights covered areas of zero activity, intense mining, drilling, ore loading, waste discharge, open stockpiling, and mineral processing. In the zero-activity area, the resuspension of PM10 was very low, with a weak wind speed (3.6 m/s). In the intense-mining area, unhealthy concentrations of PM10 (>51 µgPM10/m3) could be released, and the PM10 can reach surrounding populations through long-distance transport driven by several processes being performed simultaneously. Strong dilution was also observed at high altitudes (> 50 m). Mean concentrations were found to be 22-89 µgPM10/m3, with peaks ranging from 86 to 284 µgPM10/m3. This study demonstrates the potential applicability of airborne LCSs in the high-resolution online monitoring of PM in mining, thus supporting environmental managers during decision-making against fugitive emissions in a cost-effective manner.

The impact of COVID-19 public health restrictions on particulate matter pollution measured by a validated low-cost sensor network in Oxford, UK.

Emergency responses to the COVID-19 pandemic led to major changes in travel behaviours and economic activities with arising impacts upon urban air quality. To date, these air quality changes associated with lockdown measures have typically been assessed using limited city-level regulatory monitoring data, however, low-cost air quality sensors provide capabilities to assess changes across multiple locations at higher spatial-temporal resolution, thereby generating insights relevant for future air quality interventions. The aim of this study was to utilise high-spatial resolution air quality information utilising data arising from a validated (using a random forest field calibration) network of 15 low-cost air quality sensors within Oxford, UK to monitor the impacts of multiple COVID-19 public heath restrictions upon particulate matter concentrations (PM10, PM2.5) from January 2020 to September 2021. Measurements of PM10 and PM2.5 particle size fractions both within and between site locations are compared to a pre-pandemic related public health restrictions baseline. While average peak concentrations of PM10 and PM2.5 were reduced by 9-10 µg/m3 below typical peak levels experienced in recent years, mean daily PM10 and PM2.5 concentrations were only ∼1 µg/m3 lower and there was marked temporal (as restrictions were added and removed) and spatial variability (across the 15-sensor network) in these observations. Across the 15-sensor network we observed a small local impact from traffic related emission sources upon particle concentrations near traffic-oriented sensors with higher average and peak concentrations as well as greater dynamic range, compared to more intermediate and background orientated sensor locations. The greater dynamic range in concentrations is indicative of exposure to more variable emission sources, such as road transport emissions. Our findings highlight the great potential for low-cost sensor technology to identify highly localised changes in pollutant concentrations as a consequence of changes in behaviour (in this case influenced by COVID-19 restrictions), generating insights into non-traffic contributions to PM emissions in this setting. It is evident that additional non-traffic related measures would be required in Oxford to reduce the PM10 and PM2.5 levels to within WHO health-based guidelines and to achieve compliance with PM2.5 targets developed under the Environment Act 2021.

Calibration methodology of low-cost sensors for high-quality monitoring of fine particulate matter.

Low concentrations of pollutants may already be associated with significant health effects. An accurate assessment of individual exposure to pollutants therefore requires measuring pollutant concentrations at the finest possible spatial and temporal scales. Low-cost sensors (LCS) of particulate matter (PM) meet this need so well that their use is constantly growing worldwide. However, everyone agrees that LCS must be calibrated before use. Several calibration studies have already been published, but there is not yet a standardized and well-established methodology for PM sensors. In this work, we develop a method combining an adaptation of an approach developed for gas-phase pollutants with a dust event preprocessing to calibrate PM LCS (PMS7003) commonly used in urban environments. From the selection of outliers to model tuning and error estimation, the developed protocol allows to analyze, process and calibrate LCS data using multilinear (MLR) and random forest (RFR) regressions for comparison with a reference instrument. We demonstrate that the calibration performance was very good for PM1 and PM2.5 but turns out less good for PM10 (R2 = 0.94, RMSE = 0.55 µg/m3, NRMSE = 12 % for PM1 with MLR, R2 = 0.92, RMSE = 0.70 µg/m3, NRMSE = 12 % for PM2.5 with RFR and R2 = 0.54, RMSE = 2.98 µg/m3, NRMSE = 27 % for PM10 with RFR). Dust events removal significantly improved LCS accuracy for PM2.5 (11 % increase of R2 and 49 % decrease of RMSE) but no significant changes for PM1. Best calibration models included internal relative humidity and temperature for PM2.5 and only internal relative humidity for PM1. It turns out that PM10 cannot be properly measured and calibrated because of technical limitations of the PMS7003 sensor. This work therefore provides guidelines for PM LCS calibration. This represents a first step toward standardizing calibration protocols and facilitating collaborative research.

A yearlong monitoring campaign of polycyclic aromatic compounds and other air pollutants at three sites in Sweden: Source identification, in vitro toxicity and human health risk assessment.

Air pollution is a complex mixture of gases and particulate matter (PM) with local and non-local emission sources, resulting in spatiotemporal variability in concentrations and composition, and thus associated health risks. To study this in the greater Stockholm area, a yearlong monitoring campaign with in situ measurements of PM10, PM1, black carbon, NOx, O3, and PM10-sampling was performed. The locations included an Urban and a Rural background site and a Highway site. Chemical analysis of PM10 was performed to quantify monthly levels of polycyclic aromatic compounds (PACs), which together with other air pollution data were used for source apportionment and health risk assessment. Organic extracts from PM10 were tested for oxidative potential in human bronchial epithelial cells. Strong seasonal patterns were found for most air pollutants including PACs, with higher levels during the winter months than summer e.g., highest levels of PM10 were detected in March at the Highway site (33.2 µg/m3) and lowest in May at the Rural site (3.6 µg/m3). In general, air pollutant levels at the sites were in the order Highway > Urban > Rural. Multivariate analysis identified several polar PACs, including 6H-Benzo[cd]pyren-6-one, as possible discriminatory markers for these sites. The main sources of particulate pollution for all sites were vehicle exhaust and biomass burning emissions, although diesel exhaust was an important source at the Highway site. In vitro results agreed with air pollutant levels, with higher oxidative potential from the winter samples. Estimated lung cancer cases were in the order PM10 > NO2 > PACs for all sites, and with less evident seasonal differences than in vitro results. In conclusion, our study presents novel seasonal data for many PACs together with air pollutants more traditionally included in air quality monitoring. Moreover, seasonal differences in air pollutant levels correlated with differences in toxicity in vitro.

Health risk assessment for particulate matter: application of AirQ+ model in the northern Caribbean region of Colombia.

Air pollution is considered the world's most important environmental and public health risk. The annual exposure for particulate matter (PM) in the northern Caribbean region of Colombia between 2011 and 2019 was determined using PM records from 25 monitoring stations located within the area. The impact of exposure to particulate matter was assessed through the updated Global Burden of Disease health risk functions using the AirQ+ model for mortality attributable to acute lower respiratory disease (in children ≤ 4 years); mortality in adults aged > 18 years old attributable to chronic obstructive pulmonary disease, ischaemic heart disease, lung cancer, and stroke; and all-cause post-neonatal infant mortality. The proportions of the prevalence of bronchitis in children and the incidence of chronic bronchitis in adults attributable to PM exposure were also estimated for the population at risk. Weather Research and Forecasting-California PUFF (WRF-CALPUFF) modeling systems were used to estimate the spatiotemporal trends and calculate mortality relative risk due to prolonged PM2.5 exposure. Proportions of mortality attributable to long-term exposure to PM2.5 were estimated to be around 11.6% of ALRI deaths in children ≤ 4 years of age, 16.1% for COPD, and 26.6% for IHD in adults. For LC and stroke, annual proportions attributable to PM exposure were estimated to be 9.1% and 18.9%, respectively. An estimated 738 deaths per year are directly attributed to particulate matter pollution. The highest number of deaths per year is recorded in the adult population over 18 years old with a mean of 401 events. The mean risk in terms of the prevalence of bronchitis attributable to air pollution in children was determined to be 109 per 100,000 inhabitants per year. The maximum RR values for mortality (up 1.95%) from long-term PM2.5 exposure were predicted to correspond to regions downwind to the industrial zone. Supplementary information: The online version contains supplementary material available at 10.1007/s11869-023-01304-5.

Heavy metal content and health risk assessment of atmospheric particles in China: A meta-analysis.

In recent decades, China has devoted significant attention to the heavy metals pollution in particulate matter. However, the majority of studies have only focused on the field monitoring in relatively remote areas, which may not be representative of air quality across the country. This study reevaluated the characteristics, temporal and spatial changes, and health concerns associated with heavy metal pollution in atmospheric particulates on a national scale by coupling Meta-analysis and Monte Carlo simulation analysis. In terms of spatial distribution, the heavy metals pollution levels in the northern coast and northeastern regions are relatively high, whereas it is low along the middle Yellow River, middle Yangtze River, as well as Southwest. With the exception of Cu, the distribution of all elements in PM2.5 steadily decreased over time Moreover, PM10 and PM2.5 performed similar where Cd and Ni both first increased followed by a decline while, Cr displayed a decrease before it showed an increment. And since the implementation of prevention and control policies about the atmospheric release, the focus of industrial emission has gradually shifted from energy production and processing to living products manufacturing. Moreover, the carcinogenic risk was shown to be Cr > As, Pb > Ni, Cd, while the non-carcinogenic risk was as follows: As, Ni > Cr, Cd. Among all contaminants, Cd, As, and Cr in PM2.5 and PM10 exceeded the WHO standard in the cities with worst air quality. It was observed that As posed the largest non-carcinogenic risk to adults while, Cr caused the most carcinogenic risk to adults and children, where the carcinogenic risk of children remains higher than that of adults. Therefore, the findings of this study may offer data support to the China's heavy metal pollution standards in airborne particles and offer theoretical data support for pollution management.

Exploring the impact of particulate matter on mortality in coastal Mediterranean environments.

Air pollution is one of the most important problems the world is facing nowadays, adversely affecting public health and causing millions of deaths every year. Particulate matter is a criteria pollutant that has been linked to increased morbidity, as well as all-cause and cause-specific mortality. However, this association remains under-investigated in smaller-size cities in the Eastern Mediterranean, which are also frequently affected by heat waves and dust storms. This study explores the impact of particulate matter with an aerodynamic diameter ≤ 10 µm (PM10) and ≤ 2.5 µm (PM2.5) on mortality (all-cause, cardiovascular, respiratory) in two coastal cities in the Eastern Mediterranean; Thessaloniki, Greece and Limassol, Cyprus. Generalized additive Poisson models were used to explore overall and gender-specific associations, controlling for long- and short-term patterns, day of week and the effect of weather variables. Moreover, the effect of different lags, season, co-pollutants and dust storms on primary associations was investigated. A 10 µg/m3 increase in PM2.5 resulted in 1.10 % (95 % CI: -0.13, 2.34) increase in cardiovascular mortality in Thessaloniki, and in 3.07 % (95 % CI: -0.90, 7.20) increase in all-cause mortality in Limassol on the same day. Additionally, significant positive associations were observed between PM2.5 as well as PM10 and mortality at different lags up to seven days. Interestingly, an association with dust storms was observed only in Thessaloniki, having a protective effect, while the gender-specific analysis revealed significant associations only for the males in both cities. The outcome of this study highlights the need of city- or county-specific public health interventions to address the impact of climate, population lifestyle behaviour and other socioeconomic factors that affect the exposure to air pollution and other synergistic effects that alter the effect of PM on population health.

Green complexity, economic fitness, and environmental degradation: evidence from US state-level data.

Green production is one of the major debates as environmental degradation poses threats globally. The paper attempts to explore the relationship between green production and environmental quality by using Economic Fitness approach. We develop a Green Complexity Index (GCI) dataset consisting of 290 traded green-labeled products and Economic Fitness Index (EFI) for the US states between 2002 and 2018. We analyze the environmental performance of green production using the GCI and EFI data at the sub-national level. Findings indicate that exporting more complex green products has insignificant effects on local (i.e., sulfur dioxide, particulate Matter 10) and global polluters such as carbon dioxide, even accounting for per capita income. Yet, economic fitness has a significant negative impact on the emission levels implying that sophisticated production significantly improves environmental quality in the USA. The insignificant impact of GCI on environmental degradation suggests that green product classifications should incorporate the production and end-use stages of goods to limit the adverse environmental effects of green-labeled products.

Sociodemographic inequalities in long-term exposure to air pollution, road traffic noise, and greenness: A population-based cohort study of women.

Background: Recent evidence suggests environmental health inequalities both within and between European countries and socially deprived groups may be more susceptible to pollution. However, evidence is still inconclusive and additional studies are warranted. This study aims to investigate sociodemographic inequalities in long-term residential exposure to air pollution, road traffic noise, and greenness, taking lifestyle and degree of urbanization into account. Methods: In total 20,407 women, born 1914-48 residing in Uppsala County, Sweden, were followed between 1997 and 2017. Time-varying sociodemographic variables were obtained from registers, and questionnaires provided lifestyle information. Generalized estimating equations were used to compute beta-coefficients (ß) and 95% confidence intervals (95% CI) for associations between sociodemographic and lifestyle variables and spatial-temporal modeled particulate matter (PM2.5, PM10), nitrogen dioxide (NO2), road traffic noise and greenness. All models were additionally stratified by urbanization type. Results: Urban area residency was the most important predictor of high exposure to air pollution and noise, and to low greenness. For instance, ß for NO2 was -2.92 (95% CI = -3.00, -2.83) and -3.10 (95% CI = -3.18, -3.01) µg/m3 in suburban and rural areas, respectively, compared with urban areas. For greenness, the opposite held true with corresponding ß of 0.059 (95% CI = 0.056, 0.062) and 0.095 (95% CI = 0.092, 0.098). Within urban areas, elderly, unmarried and well-educated women had the highest environmental burden. However, less pronounced, and even reversed associations were found in suburban and rural areas. Conclusion: This study provides evidence of a mixed pattern of environmental health inequalities across sociodemographic groups in urban areas.

Health risk assessment of heavy metals in PM2.5 and PM10 in Sarajevo air, Bosnia and Herzegovina.

The concentrations of eight heavy metals (Cd, Cr, Cu, Fe, Mn, Ni, Pb and Zn) associated with PM2.5 and PM10 in Sarajevo air, Bosnia and Herzegovina (BiH) have been studied. A total of 136 PM2.5 and PM10 samples were simultaneously collected from 21 February to 11 November 2020. Metal contents were determined by atomic absorption spectrometry, flame (FAAS) and electrothermal (ETAAS) techniques. The mean concentrations of metals in PM10 are 2.93 ng/m3 (Cd), 7.21 ng/m3 (Cr), 12.02 ng/m3 (Cu), 126 ng/m3 (Fe), 20.74 ng/m3 (Mn), 6.98 ng/m3 (Ni), 8.74 ng/m3 (Pb) and 128 ng/m3 (Zn). In PM2.5 samples the mean concentrations of Cd, Cr, Cu, Fe, Mn, Ni and Zn are 0.39, 4.06, 2.26, 110, 0.63, 1.93 and 5.28 ng/m3, respectively. Pb was not detected in PM2.5 samples. Strong correlation was obtained for metal pairs Mn-Cu in PM10 and moderate for Ni-Fe in PM2.5. The health risk assessment shows that the adult population of Sarajevo is at increased lifetime risk of experiencing cancer because of exposure to Cd concentrations in PM10.

Prediction and assessment of the impact of COVID-19 lockdown on air quality over Kolkata: a deep transfer learning approach.

The present study focuses on the prediction and assessment of the impact of lockdown because of coronavirus pandemic on the air quality during three different phases, viz., normal periods (1 January 2018-23 March 2020), complete lockdown (24 March 2020-31 May 2020), and partial lockdown (1 June 2020-30 September 2020). We identify the most important air pollutants influencing the air quality of Kolkata during three different periods using Random Forest, a tree-based machine learning (ML) algorithm. It is found that the ambient air quality of Kolkata is mainly affected with the aid of particulate matter or PM (PM10 and PM2.5). However, the effect of the lockdown is most prominent on PM2.5 which spreads in the air of Kolkata due to diesel-driven vehicles, domestic and commercial combustion activities, road dust, and open burning. To predict urban PM2.5 and PM10 concentrations 24 h in advance, we use a deep learning (DL) model, namely, stacked-bidirectional long short-term memory (stacked-BDLSTM). The model is trained during the normal periods, and it shows the superiority over some supervised ML models, like support vector machine, K-nearest neighbor classifier, multilayer perceptron, long short-term memory, and statistical time series forecasting model autoregressive integrated moving average. This pre-trained stacked-BDLSTM is applied to predict the concentrations of PM2.5 and PM10 during the pandemic situation of two cases, viz., complete lockdown and partial lockdown using a deep model-based transfer learning (TL) approach (TLS-BDLSTM). Transfer learning aims to utilize the information gained from one problem to improve the predictive performance of a learning model for a different but related problem. Our work helps to demonstrate how TL is useful when there is a scarcity of data during the COVID-19 pandemic regarding the drastic change in concentration of pollutants. The results reveal the best prediction performance of TLS-BDLSTM with a lead time of 24 h as compared to some well-known traditional ML and statistical models and the pre-trained stacked-BDLSTM. The prediction is then validated using the real-time data obtained during the complete lockdown due to COVID second wave (16 May-15 June 2021) with different time steps, e.g., 24 h, 48 h, 72 h, and 96-120 h. TLS-BDLSTM involving transfer learning is seen to outperform the said comparing methods in modeling the long-term temporal dependency of multivariate time series data and boost the forecast efficiency not only in single step, but also in multiple steps. The proposed methodologies are effective, consistent, and can be used by operational organizations to utilize in monitoring and management of air quality.

Seasonal changes and respiratory deposition flux of PM2.5 and PM10 bound metals in Dhaka, Bangladesh.

Due to rapid urbanization and fast economic development, aerosol pollution is a serious environmental issue, especially in Bangladesh. Based on bioaccessibility and respiratory deposition doses (RDD), health risks of PM2.5 and PM10 bound 15 (fifteen) metals were investigated at fourteen urban sites (roadside, marketplace, industrial, and commercial areas). Sampling campaigns were conducted over four seasons (winter, summer, rainy, and autumn) from December 2020 to November 2021. A beta attenuation mass analyzer measured particulate matter concentrations in ambient air. The metals in PM fractions were analyzed by X-ray fluorescence spectroscopy and inductively coupled plasma mass spectrometry (ICP-MS). The airborne trace metals (Cd, As, Zn, Pb, Cr, Cu, Ni) with high enrichment factors indicate anthropogenic sources. The positive matrix factorization (PMF) categorized these elements as originating from automobile exhaust, industrial emissions, and solid waste/coal combustion, whereas the geologic elements came from earth crust/soil dust. During the winter, most of the air mass trajectories arrived from India across the land (82%) and Indo Gangetic Plain (IGP) region to the sampling sites, which may have aided in the transport of pollutants. The deposition flux of metals illustrated that compared to PM2.5, PM10 deposited a higher amount of metals in the upper airways (81.96%). In comparison, PM2.5 accumulates more elevated amounts of metals in alveolar regions (11.77%), due to the ability of fine particles to penetrate deeper into the lower pulmonary region. Among age groups, an adult inhales a higher amount of metals than a child, on average 0.103 mg and 0.08 mg of metals per day via PM2.5, respectively. Acute health impacts are caused by the deposited cancer-causing metals in alveolar tissue, which circulates through the bloodstream and affects several organs. Prolonged exposure to these carcinogenic metals poses significant health risks.

Machine Learning and Meteorological Normalization for Assessment of Particulate Matter Changes during the COVID-19 Lockdown in Zagreb, Croatia.

In this paper, the authors investigated changes in mass concentrations of particulate matter (PM) during the Coronavirus Disease of 2019 (COVID-19) lockdown. Daily samples of PM1, PM2.5 and PM10 fractions were measured at an urban background sampling site in Zagreb, Croatia from 2009 to late 2020. For the purpose of meteorological normalization, the mass concentrations were fed alongside meteorological and temporal data to Random Forest (RF) and LightGBM (LGB) models tuned by Bayesian optimization. The models' predictions were subsequently de-weathered by meteorological normalization using repeated random resampling of all predictive variables except the trend variable. Three pollution periods in 2020 were examined in detail: January and February, as pre-lockdown, the month of April as the lockdown period, as well as June and July as the "new normal". An evaluation using normalized mass concentrations of particulate matter and Analysis of variance (ANOVA) was conducted. The results showed that no significant differences were observed for PM1, PM2.5 and PM10 in April 2020-compared to the same period in 2018 and 2019. No significant changes were observed for the "new normal" as well. The results thus indicate that a reduction in mobility during COVID-19 lockdown in Zagreb, Croatia, did not significantly affect particulate matter concentration in the long-term..