Surface Ozone Concentration and Its Relationship with UV Radiation, Meteorological Parameters and Radon on the Eastern Coast of the Baltic Sea

Photo-modified and photo-degradable starch/Fe3O4/TiO2 nanocomposite: exploring the feasibility of reducing workforce by magnetic recycling

Plastics are known for their durability and long decomposition time in the environment, which make plastic recycling an effective approach to mitigate plastic waste risks. However, the global plastic recycling rate is less than 10% mainly due to the labor-intensive and time-consuming nature of the manual recycling process, which poses high health risks and costs. Therefore, the development of a fast, effective, and operational process in current recycling plants is crucial to address the environmental concerns associated with plastics. In the current study, the feasibility of starch/Fe3O4/TiO2 bio-nanocomposite (SFT) as photo-modifiable and photo-degradable was investigated to reduce the workforce in recycling packaging material. The SFT was modified by different UV-C exposure times, which significantly altered its functional properties. The UV-C exposure increased the hydrophobicity of the SFT films and led to a homogenous distribution of Fe3O4/TiO2 nanoparticles (FT). It also increased tensile strength (TS) and decreased elongation at break (EB) of the films. It seems that producing shorter polymer chains, creating new linkages among the polymeric chains, and the homogenous distribution of FT in the matrix of biopolymer by UV-C are the main reasons for these changes. Moreover, the photo-degradation of SFT specimens increased significantly with longer UV-C exposure times. The utilization of magnetic properties in bio-based nanocomposites holds promising potential for streamlining labor-intensive processes in waste recycling plants. However, the inappropriate visual properties of SFT remain a significant obstacle that requires further attention to enable its commercial viability.

Cl atoms-initiated degradation of 1-Chlorobutane and 2-Chlorobutane: Kinetics, product analysis and atmospheric implications

The relative rate method was employed to investigate the kinetics of the Cl-initiated reactions of 1-chlorobutane (1-CB) and 2-chlorobutane (2-CB) over 263-363 K, and the measured rate coefficients at room temperature are (1.04 ± 0.24) × 10-10 and (5.84 ± 0.27) × 10-11 cm3 molecule-1 s-1, respectively. The Arrhenius equations for the title reactions were derived to be k1-CB + Cl (T = 263-363 K) = (2.77 ± 0.72) × 10-11 exp [(422 ± 79)/T] and k2-CB + Cl (T = 263-363 K) = (1.40 ± 0.32) × 10-11 exp [(415 ± 70)/T] cm3 molecule-1 s-1, respectively. The products were analysed qualitatively using gas chromatography-mass spectrometry (GC-MS), and the reaction mechanism was proposed for the reactions. The rate coefficients for the title reactions were calculated computationally over the temperature range of 200-400 K using canonical variational transition state theory with appropriate tunnelling corrections at CCSD(T)/6-311++G(2d,2p)//BHandHLYP/6-311++G(2d,2p) level of theory to complement our experimentally measured kinetic parameters. The experimental and theoretical data obtained were used to evaluate the impact of the studied molecules in the troposphere.

UV radiation and air pollution as drivers of major autoimmune conditions

Autoimmune diseases comprise a very heterogeneous group of disorders characterized by disruptive immune responses against self-antigens, chronic morbidity and increased mortality. The incidence and prevalence of major autoimmune conditions are particularly high in the western world, at northern latitudes, and in industrialized countries. This study will mainly focus on five major autoimmune conditions, namely type 1 diabetes, multiple sclerosis, inflammatory bowel diseases, rheumatoid arthritis, and autoimmune thyroid disorders. Epidemiological and experimental evidence suggests a protective role of sunlight exposure on the etiology of major autoimmune conditions mediated by the endogenous production of vitamin D and nitric oxide. A historical perspective shows how the rise of anthropogenic air pollutants is temporally associated with dramatic increases in incidence of these conditions. The scattering caused by ambient particulate matter and the presence of tropospheric ozone can reduce the endogenous production of vitamin D and nitric oxide, which are implicated in maintaining the immune homeostasis. Air pollutants have direct detrimental effects on the human body and are deemed responsible of an increasingly higher portion of the annual burden of human morbidity and mortality. Air pollution contributes in systemic inflammation, activates oxidative pathways, induces epigenetic alterations, and modulates the function and phenotype of dendritic cells, Tregs, and T-cells. In this review, we provide epidemiological and mechanistic insights regarding the role of UV-mediated effects in immunity and how anthropic-derived air pollution may affect major autoimmune conditions through direct and indirect mechanisms.

Factors Influencing O3 Concentration in Traffic and Urban Environments: A Case Study of Guangzhou City

Ozone (O3) pollution is a serious issue in China, posing a significant threat to people's health. Traffic emissions are the main pollutant source in urban areas. NOX and volatile organic compounds (VOCs) from traffic emissions are the main precursors of O3. Thus, it is crucial to investigate the relationship between traffic conditions and O3 pollution. This study focused on the potential relationship between O3 concentration and traffic conditions at a roadside and urban background in Guangzhou, one of the largest cities in China. The results demonstrated that no significant difference in the O3 concentration was observed between roadside and urban background environments. However, the O3 concentration was 2 to 3 times higher on sunny days (above 90 μg/m3) than on cloudy days due to meteorological conditions. The results confirmed that limiting traffic emissions may increase O3 concentrations in Guangzhou. Therefore, the focus should be on industrial, energy, and transportation emission mitigation and the influence of meteorological conditions to minimize O3 pollution. The results in this study provide some theoretical basis for mitigation emission policies in China.

Modelling and investigating the impacts of climatic variables on ozone concentration in Malaysia using correlation analysis with random forest, decision tree regression, linear regression, and support vector regression

Climate change is generally known to impact ozone concentration globally. However, the intensity varies across regions and countries. Therefore, local studies are essential to accurately assess the correlation of climate change and ozone concentration in different countries. This study investigates the effects of climatic variables on ozone concentration in Malaysia in order to understand the nexus between climate change and ozone concentration. The selected data was obtained from ten (10) air monitoring stations strategically mounted in urban-industrial and residential areas with significant emissions of pollutants. Correlation analysis and four machine learning algorithms (random forest, decision tree regression, linear regression, and support vector regression) were used to analyze ozone and meteorological dataset in the study area. The analysis was carried out during the southwest monsoon due to the rise of ozone in the dry season. The results show a very strong correlation between temperature and ozone. Wind speed also exhibits a moderate to strong correlation with ozone, while relative humidity is negatively correlated. The highest correlation values were obtained at Bukit Rambai, Nilai, Jaya II Perai, Ipoh, Klang and Petaling Jaya. These locations have high industries and are well urbanized. The four machine learning algorithms exhibit high predictive performances, generally ascertaining the predictive accuracy of the climatic variables. The random forest outperformed other algorithms with a very high R² of 0.970, low RMSE of 2.737 and MAE of 1.824, followed by linear regression, support vector regression and decision tree regression, respectively. This study's outcome indicates a linkage between temperature and wind speed with ozone concentration in the study area. An increase of these variables will likely increase the ozone concentration posing threats to lives and the environment. Therefore, this study provides data-driven insights for decision-makers and other stakeholders in ensuring good air quality for sustainable cities and communities. It also serves as a guide for the government for necessary climate actions to reduce the effect of climate change on air pollution and enabling sustainable cities in accordance with the UN's SDGs 13 and 11, respectively.

Atmospheric pollutants response to the emission reduction and meteorology during the COVID-19 lockdown in the north of Africa (Morocco)

Climate and air quality change due to COVID-19 lockdown (LCD) are extremely concerned subjects of several research recently. The contribution of meteorological factors and emission reduction to air pollution change over the north of Morocco has been investigated in this study using the framework generalized additive models, that have been proved to be a robust technique for the environmental data sets, focusing on main atmospheric pollutants in the region including ozone (O3), nitrogen dioxide (NO2), sulfur dioxide (SO2), particulate matter (PM2.5 and PM10), secondary inorganic aerosols (SIA), nom-methane volatile organic compounds and carbon monoxide (CO) from the regional air pollution dataset of the Copernicus Atmosphere Monitoring Service. Our results, indicate that secondary air pollutants (PM2.5, PM10 and O3) are more influenced by metrological factors and the other air pollutants reported by this study (NO2 and SO2). We show a negative effect for PBHL, total precipitation and NW10M on PM (PM2.5 and PM10 ), this meteorological parameters contribute to decrease in PM2.5 by 9, 2 and 9% respectively, before LCD and 8, 1 and 5% respectively during LCD. However, a positive marginal effect was found for SAT, Irradiance and RH that contribute to increase PM2.5 by 9, 12 and 18% respectively, before LCD and 17, 54 and 34% respectively during LCD. We found also that meteorological factors contribute to O3, PM2.5, PM10 and SIA average mass concentration by 22, 5, 3 and 34% before LCD and by 28, 19, 5 and 42% during LCD respectively. The increase in meteorological factors marginal effect during LCD shows the contribution of photochemical oxidation to air pollution due to increase in atmospheric oxidant (O3 and OH radical) during LCD, which can explain the response of PM to emission reduction. This study indicates that PM (PM2.5, PM10) has more controlled by SO2 due to the formation of sulfate particles especially under high oxidants level. The positive correlation between westward wind at 10 m (WW10M), Northward Wind at 10 m (NW10M) and PM indicates the implication of sea salt particles transported from Mediterranean Sea and Atlantic Ocean. The Ozone mass concentration shows a positive trend with Irradiance, Total and SAT during LCD; because temperature and irradiance enhance tropospheric ozone formation via photochemical reaction.This study shows the contribution of atmospheric oxidation capacity to air pollution change.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00477-022-02224-z.

Mechanisms and Pathways for Coordinated Control of Fine Particulate Matter and Ozone

PURPOSE OF REVIEW

Fine particulate matter (PM_2.5) and ground-level ozone (O₃) pose a significant risk to human health. The World Health Organization (WHO) has recently revised healthy thresholds for both pollutants. The formation and evolution of PM_2.5 and O₃ are however governed by complex physical and multiphase chemical processes, and therefore, it is extremely challenging to mitigate both pollutants simultaneously. Here, we review mechanisms and discuss the science-informed pathways for effective and simultaneous mitigation of PM_2.5 and O₃.

RECENT FINDINGS

Global warming has led to a general increase in biogenic emissions, which can enhance the formation of O₃ and secondary organic aerosols. Reductions in anthropogenic emissions during the COVID-19 lockdown reduced PM_2.5; however, O₃ was enhanced in several polluted regions. This was attributed to more intense sunlight due to low aerosol loading and non-linear response of O₃ to NO _x . Such contrasting physical and chemical interactions hinder the formulation of a clear roadmap for clean air over such regions.

SUMMARY

Atmospheric chemistry including the role of biogenic emissions, aerosol-radiation interactions, boundary layer, and regional-scale transport are the key aspects that need to be carefully considered in the formulation of mitigation pathways. Therefore, a thorough understanding of the chemical effects of the emission reductions, changes in photolytic rates and boundary layer due to perturbation of solar radiation, and the effect of meteorological/seasonal changes are needed on a regional basis. Statistical emulators and machine learning approaches can aid the cumbersome process of multi-sector multi-species source attribution.

Evaluation of the Influence between Local Meteorology and Air Quality in Beijing Using Generalized Additive Models

Previous studies have confirmed the inextricable connection between meteorological factors and air pollutants. This study presents the complex nonlinear relationship between meteorological variables and four major air pollutants under high-concentration air pollution in Beijing. The generalized additive model combined with marginal effects is used for quantitative analysis. After controlling the confounding factors such as long-term trends, seasonality and spatio-temporal deviation, the final fitting results exhibit that temperature, relative humidity and visibility are the most significant meteorological variables associating with PM2.5 concentration, and the marginal effect reaches 80%, −23% and 270%, respectively. Temperature and relative humidity are the most significant variables for SO2, and the marginal effect reaches 15% and 7%. The most significant variables for O3 are temperature and solar radiation, with marginal effect of up to 70% and 8%. Atmospheric pressure and temperature results in a positive effect on CO, and the marginal effect can reach 18% and 80%. All these indicate that local meteorological variables are a significant driving factor for air quality in Beijing. Other variables, such as wind speed, visibility, and precipitation, display some influence on air pollutants, but have less explanatory power in the model. Overall, our study provides a better understanding of the relationship between local meteorological variables and air quality, as well as an insight into how climate change affects air quality.

Spatiotemporal evolution analysis of NO2 column density before and after COVID-19 pandemic in Henan province based on SI-APSTE model

Air pollution is the result of comprehensive evolution of a dynamic and complex system composed of emission sources, topography, meteorology and other environmental factors. The establishment of spatiotemporal evolution model is of great significance for the study of air pollution mechanism, trend prediction, identification of pollution sources and pollution control. In this paper, the air pollution system is described based on cellular automata and restricted agents, and a Swarm Intelligence based Air Pollution SpatioTemporal Evolution (SI-APSTE) model is constructed. Then the spatiotemporal evolution analysis method of air pollution is studied. Taking Henan Province before and after COVID-19 pandemic as an example, the NO₂ products of TROPOMI and OMI were analysed based on SI-APSTE model. The tropospheric NO₂ Vertical Column Densities (VCDs) distribution characteristics of spatiotemporal variation of Henan province before COVID-19 pandemic were studied. Then the tropospheric NO₂ VCDs of TROPOMI was used to study the pandemic period, month-on-month and year-on-year in 18 urban areas of Henan Province. The results show that SI-APSTE model can effectively analyse the spatiotemporal evolution of air pollution by using environmental big data and swarm intelligence, and also can establish a theoretical basis for pollution source identification and trend prediction.

Impact of novel coronavirus disease (COVID-19) lockdown on ambient air quality of Saudi Arabia

The outbreak of COVID-19 has spread globally affecting human activities but with improvement in ambient air quality. The first case of the virus in the Kingdom of Saudi Arabia was on the 2nd of March 2020. The impact of COVID-19 lockdown on the ambient air quality of the Kingdom of Saudi Arabia for the first time using data from nine cities was determined in this study. Hourly air quality data, based on concentrations of carbon monoxide (CO), particulate matter (PM10), sulfur dioxide (SO2), nitrogen dioxide (NO2) and ozone (O3), and meteorological conditions (atmospheric temperature, relative humidity, and wind speed) of the nine cities studied were obtained from Saudi Arabian General Authority of Meteorology and Environmental Protection (GAMEP), for the period between January 2019 to May 2020. Significant variation (p < 0.05) was recorded for the five atmospheric pollutants across the cities before and during the lockdown, with lower concentrations during the lockdown except for the concentration of O3 in Tabuk, Al Qasim, and Haql. This can be a result of NO and O3 reaction, causing the inability of effective O3 depletion. The percentage changes in concentrations of CO (33.60%) and SO2 (44.16%) were higher in Jeddah; PM10 (91.12%) in Riyadh, while NO2 (44.35%) and O3 (18.98%) were highest in Makkah. However, even though there was a decrease in pollutants concentrations during the lockdown, the concentrations for CO, PM10, SO2, NO2, and O3 were still above WHO 24 h and annual mean limit levels. The COVID-19 lockdown in the Kingdom of Saudi Arabia revealed the possibility of significant atmospheric pollutant reduction by controlling traffic, activities by industries, and environmentally friendly transportation programs such as green commuting programs.

Urban Ground-Level O3 Trends: Lessons from Portuguese Cities, 2010–2018

Big datasets of air-quality pollutants and weather data allow us to review trends of NO2, NO, O3, and global radiation (GR), for Lisbon, Porto and Coimbra, with regard to the historical period of 2010–2018. GR is expected to have a considerable impact on photochemical reactions of the O3 formation mechanism. We aim to characterize daily, monthly, and yearly trends. We explore Weekday (WD) versus weekend (WE), and seasonality of O3 and NO2. We are interested to know these pollutant peak concentration variations over the years and investigate if parallels can be drawn between urban mobility indicators and these pollutants. For this purpose, economic data, European emission standards, and car stock data (fuel, age, and number of vehicles) are cross-analyzed. How are they correlated? Has it impacted NO2 and O3 variations? How do different air-quality monitoring stations (AQMS), traffic and non-traffic, compare? How is Lisbon NOx-O3 correlated? What are its implications for future scenarios? Results show that urban mobility trends and economic events are correlated with NO2 and O3 variability. Weekend effect has a partial relationship with urban mobility trends and economy as it is relatively well correlated for Lisbon but not for Porto and Coimbra. Nonetheless, weekend effect for the period of 2010–2018 is overall trending upwards for all cities. In Lisbon and Coimbra, O3 concentrations also trend upwards during the same 2010–2018 period but for Porto they do not. Regardless, for the period of 2015–2018, after the economic recession, the upwards trends of both weekend effect and overall O3 concentrations are clear for all AQMS. For AQMS peak values comparison, Lisbon traffic AQMS registered an annual averaged 8-hour daily max O3 concentration of 34.4 ppb while Lisbon non-traffic AQMS presented 39.1 ppb. Altogether, annual 8-hour daily maximum values for 2010–2018 traffic AQMS in Lisbon show an inverse relationship with fuel sales, and have concentrations fluctuating between 28–35 ppb, which is slightly higher than the 2001–2010 historical European range of 27–31 ppb. Lastly, for the 8 years data in Lisbon, it has been shown that a negative NOx-O3 correlation exists, and the study location might be VOC–sensitive. This means that as NOx concentrations decrease, O3 concentrations become exponentially higher. Further research into VOCs with better data availability is required to make more concise claims. Regardless, it can be inferred that in a future scenario where mitigation continues to escalate, through O3 emission standards and an aggressive shift of car stock to electric vehicles, achieving unprecedented rises in O3 concentrations could be observed.

Accumulation of Airborne Toxic Elements and Photosynthetic Performance of Lolium multiflorum L. Leaves

In this study, we aimed to investigate the accumulation of airborne trace elements in Lolium multiflorum leaves concerning photosynthetic activity parameters. Five sites for four 28-day series of plant exposition were selected. The concentration of trace elements in leaves was measured after each series, while photosynthetic activity parameters were measured three times during each series. Net photosynthesis rate (PN) and stomatal conductance (gs) were mostly negatively associated with all analyzed trace elements, unlike to CO2 concentrations (Ci). Arsenic was found with opposite trend in two exposure series. The high accumulation of Cd and Pb in plants recorded at two sites was mostly related to lowest PN and gs. Similar tendency for PN was found at sites and series with the highest Cr and Ni content in plants. L. multiflorum revealed a medium-level accumulation of trace elements and a low tolerance of the photosynthetic process to the presence of trace elements in ambient air.

Risk assessment of human exposure to Ra-226 in oil produced water from the Bakken Shale

Unconventional oil production in North Dakota (ND) and other states in the United States uses large amounts of water for hydraulic fracturing to stimulate oil flow. Most of the water used returns to the surface as produced water (PW) containing different constituents. Some of these contents are total dissolved solids and radionuclides. The most predominant radionuclide in PW is radium-226 (Ra-226) of which level depends on several factors including the content of certain cations. A multivariate regression model was developed to predict Ra-226 in PW from the Bakken Shale based on the levels of barium, strontium, and calcium. The simulated Ra-226 activity concentration in PW was 535 pCi/L supporting extremely limited actual data based on three PW samples from the Bakken (527, 816, and 1210 pCi/L). The simulated activity concentration was further analyzed by studying its impact in the event of a PW spill reaching a surface water body that provides drinking water, irrigation water for crops, and recreational fishing. Using food transfer factors found in the literature, the final annual effective dose rate for an adult in ND was estimated. The global average annual effective dose rate via food and drinking water is 0.30 mSv, while the predicted dose rate in this study was 0.49 mSv indicating that there is potential risk to human health in ND due to Ra-226 in PW spills. This predicted dose rate is considered the best case scenario as it is based on the simulated Ra-226 activity concentration in PW of 535 pCi/L which is close to the low end actual activity concentration of 527 pCi/L.