Comprehensive analysis of air pollution and the influence of meteorological factors: a case study of adiyaman province.

Adiyaman, a city recently affected by an earthquake, is facing significant air pollution challenges due to both anthropogenic activities and natural events. The sources of air pollution have been investigated using meteorological variables. Elevated southerly winds, especially prominent in spring and autumn, significantly contribute to dust transport, leading to a decline in local air quality as detected by the HYSPLIT model. Furthermore, using Suomi-NPP Thermal Anomaly satellite product, it is detected and analyzed for crop burning activities. Agricultural practices, including stubble burning, contribute to the exacerbation of PM10 pollution during the summer months, particularly when coupled with winds from all directions except the north. In fall and winter months, heating is identified as the primary cause of pollution. The city center located north of the station is the dominant source of pollution throughout all seasons. The study established the connection between air pollutants and meteorological variables. Furthermore, the Spearman correlation coefficients reveal associations between PM10 and SO2, indicating moderate positive correlations under pressure conditions (r = 0.35, 0.52). Conversely, a negative correlation is observed with windspeed (r = -0.35, -0.50), and temperature also exhibits a negative correlation (r = -0.39, -0.54). During atmospheric conditions with high pressure, PM10 and SO2 concentrations are respectively 41.2% and 117.2% higher. Furthermore, pollutant concentration levels are 29.2% and 53.3% higher on days with low winds. Last, practical strategies for mitigating air pollution have been thoroughly discussed and proposed. It is imperative that decision-makers engaged in city planning and renovation give careful consideration to the profound impact of air pollution on both public health and the environment, particularly in the aftermath of a recent major earthquake.

Does "National Civilized City" policy mitigate air pollution in China? A spatial Durbin difference-in-differences analysis.

"National Civilized City" (NCC) is regarded as China's highest honorary title and most valuable city brand. To win and maintain the "golden city" title, municipal governments must pay close attention to various key appraisal indicators, mainly environmental ones. In this study we verify whether cities with the title are more likely to mitigate SO2 pollution. We adopt the spatial Durbin difference-in-differences (DID) model and use panel data of 283 Chinese cities from 2003 to 2018 to analyze the local (direct) and spillover effects (indirect) of the NCC policy on SO2 pollution. We find that SO2 pollution in Chinese cities is not randomly distributed in geography, suggesting the existence of spatial spillovers and possible biased estimates. Our study treats the NCC policy as a quasi-experiment and incorporates spatial spillovers of NCC policy into a classical DID model to verify this assumption. Our findings show: (1) The spatial distribution of SO2 pollution represents strong spatial spillovers, with the most highly polluted regions mainly situated in the North China Plain. (2) The Moran's I test results confirms significant spatial autocorrelation. (3) Results of the spatial Durbin DID models reveal that the civilized cities have indeed significantly mitigated SO2 pollution, indicating that cities with the honorary title are acutely aware of the environment in their bid to maintain the golden city brand. As importantly, we notice that the spatial DID term is also significant and negative, implying that neighboring civilized cities have also mitigated their own SO2 pollution. Due to demonstration and competition effects, neighboring cities that won the title ostensibly motivates local officials to adopt stringent policies and measures for lowering SO2 pollution and protecting the environment in competition for the golden title. The spatial autoregressive coefficient was significant and positive, indicating that SO2 pollution of local cities has been deeply affected by neighbors. A series of robustness check tests also confirms our conclusions. Policy recommendations based on the findings for protecting the environment and promoting sustainable development are proposed.

Experimental assessment of pollutant emissions from residential fuel cells and comparative benchmark analysis.

Energy transition currently brings focus on fuel cell micro-combined heat and power (mCHP) systems for residential uses. The two main technologies already commercialized are the Proton Exchange Membrane Fuel Cells (PEMFCs) and Solid Oxide Fuel Cells (SOFCs). The pollutant emissions of one system of each technology have been tested with a portable probe both in laboratory and field-test configurations. In this paper, the nitrogen oxides (NOx), sulphur dioxide (SO2), and carbon monoxide (CO) emission levels are compared to other combustion technologies such as a recent Euro 6 diesel automotive vehicle, a classical gas condensing boiler, and a gas absorption heat pump. At last, a method of converting the concentration of pollutants (in ppm) measured by the sensors into pollutant intensity per unit of energy (in mg/kWh) is documented and reported. This allows for comparing the pollutant emissions levels with relevant literature, especially other studies conducted with other measuring sensors. Both tested residential fuel cell technologies fed by natural gas can be considered clean regarding SO2 and NOx emissions. The CO emissions can be considered quite low for the tested SOFC and even nil for the tested PEMFC. The biggest issue of natural gas fuel cell technologies still lies in the carbon dioxide (CO2) emissions associated with the fossil fuel they consume. The gas absorption heat pump however shows worse NOx and CO levels than the classical gas condensing boiler. At last, this study illustrates that the high level of hybridization between a fuel cell and a gas boiler may be responsible for unexpected ON/OFF cycling behaviours and therefore prevent both sub-systems from operating as optimally and reliably as they would have as standalone units.

Effects of extreme meteorological factors and high air pollutant concentrations on the incidence of hand, foot and mouth disease in Jining, China.

Background: The evidence on the effects of extreme meteorological conditions and high air pollution levels on incidence of hand, foot and mouth disease (HFMD) is limited. Moreover, results of the available studies are inconsistent. Further investigations are imperative to elucidate the specific issue. Methods: Data on the daily cases of HFMD, meteorological factors and air pollution were obtained from 2017 to 2022 in Jining City. We employed distributed lag nonlinear model (DLNM) incorporated with Poisson regression to explore the impacts of extreme meteorological conditions and air pollution on HFMD incidence. Results: We found that there were nonlinear relationships between temperature, wind speed, PM2.5, SO2, O3 and HFMD. The cumulative risk of extreme high temperature was higher at the 95th percentile (P95th) than at the 90th percentile(P90th), and the RR values for both reached their maximum at 10-day lag (P95th RR = 1.880 (1.261-2.804), P90th RR = 1.787 (1.244-2.569)), the hazardous effect of extreme low temperatures on HFMD is faster than that of extreme high temperatures. The cumulative effect of extreme low wind speeds reached its maximum at 14-day lag (P95th RR = 1.702 (1.389-2.085), P90th RR = 1.498(1.283-1.750)). The cumulative effect of PM2.5 concentration at the P90th was largest at 14-day lag (RR = 1.637 (1.069-2.506)), and the cumulative effect at the P95th was largest at 10-day lag (RR = 1.569 (1.021-2.411)). High SO2 concentration at the P95th at 14-day lag was associated with higher risk for HFMD (RR: 1.425 (1.001-2.030)). Conclusion: Our findings suggest that high temperature, low wind speed, and high concentrations of PM2.5 and SO2 are associated with an increased risk of HFMD. This study not only adds insights to the understanding of the impact of extreme meteorological conditions and high levels of air pollutants on HFMD incidence but also holds practical significance for the development and enhancement of an early warning system for HFMD.

Temporal dynamics of urban air pollutants and their correlation with associated meteorological parameters: an investigation in northern Indian cities.

This study delves into the intricate dynamics of air pollution in the rapidly expanding northern regions of India, examining the intertwined influences of agricultural burning, industrialization, and meteorological conditions. Through comprehensive analysis of key pollutants (PM2.5, PM10, NO2, SO2, CO, O3) across ten monitoring stations in Uttar Pradesh, Haryana, Delhi, and Punjab, a consistent pattern of high pollution levels emerges, particularly notable in Delhi. Varanasi leads in SO2 and O3 concentrations, while Moradabad stands out for CO levels, and Jalandhar for SO2 concentrations. The study further elucidates the regional distribution of pollutants, with Punjab receiving significant contributions from SW, SE, and NE directions, while Haryana and Delhi predominantly face air masses from SE and NE directions. Uttar Pradesh's pollution sources are primarily local, with additional inputs from various directions. Moreover, significant negative correlations (p < 0.05) between PM10, NO2, SO2, O3, and relative humidity (RH) underscore the pivotal role of meteorological factors in shaping pollutant levels. Strong positive correlations between PM2.5 and NO2 (0.71 to 0.93) suggest shared emission sources or similar atmospheric conditions in several cities. This comprehensive understanding highlights the urgent need for targeted mitigation strategies to address the multifaceted drivers of air pollution, ensuring the protection of public health and environmental sustainability across the region.

Assessing Tunisia's urban air quality using combined lichens and Sentinel-5 satellite integration.

In Tunisia, urban air pollution is becoming a bigger problem. This study used a combined strategy of biomonitoring with lichens and satellite mapping with Sentinel-5 satellite data processed in Google Earth Engine (GEE) to assess the air quality over metropolitan Tunis. Lichen diversity was surveyed across the green spaces of the Faculty of Science of Tunisia sites, revealing 15 species with a predominance of pollution-tolerant genera. The Index of Atmospheric Purity (IAP) calculated from the lichen data indicated poor air quality. Spatial patterns of pollutants sulfur dioxide (SO2), ozone (O3), nitrogen dioxide (NO2), carbon monoxide (CO), and aerosol index across Greater Tunis were analyzed from Sentinel-5 datasets on the GEE platform. The higher values of these indices in the research area indicate that it may be impacted by industrial activity and highlight the considerable role that vehicle traffic plays in air pollution. The results of the IAP, IBL, and the combined ground-based biomonitoring and satellite mapping techniques confirm poor air quality and an environment affected by atmospheric pollutants which will enable proactive air quality management strategies to be put in place in Tunisia's rapidly expanding cities.

Prenatal and early life exposure to air pollution and the risk of severe lower respiratory tract infections during early childhood: the Espoo Cohort Study.

BACKGROUND: There is inconsistent evidence of the effects of exposure to ambient air pollution on the occurrence of lower respiratory tract infections (LRTIs) in early childhood. We assessed the effects of individual-level prenatal and early life exposure to air pollutants on the risk of LRTIs in early life. METHODS: We studied 2568 members of the population-based Espoo Cohort Study born between 1984 and 1990 and living in 1991 in the City of Espoo, Finland. Exposure assessment was based on dispersion modelling and land-use regression for lifetime residential addresses. The outcome was a LRTI based on data from hospital registers. We applied Poisson regression to estimate the incidence rate ratio (IRR) of LTRIs, contrasting incidence rates in the exposure quartiles to the incidence rates in the first quartile. We used weighted quantile sum (WQS) regression to estimate the joint effect of the studied air pollutants. RESULTS: The risk of LRTIs during the first 2 years of life was significantly related to exposure to individual and multiple air pollutants, measured with the Multipollutant Index (MPI), including primarily sulphur dioxide (SO2), particulate matter with a dry diameter of up to 2.5 µm (PM2.5) and nitrogen dioxide (NO2) exposures in the first year of life, with an adjusted IRR of 1.72 per unit increase in MPI (95% CI 1.20 to 2.47). LRTIs were not related to prenatal exposure. CONCLUSIONS: We provide evidence that ambient air pollution exposure during the first year of life increases the risk of LRTIs during the first 2 years of life. SO2, PM2.5 and NO2 were found to contribute the highest weights on health effects.

[Applications of ion chromatography for the analysis of Chinese herbal medicine components].

Ion chromatography (IC) is a novel high performance liquid chromatographic technique that is suitable for the separation and analysis of ionic substances in different matrix samples. Since 1975, it has been widely used in many fields, such as the environment, energy, food, and medicine. IC compensates for the separation limitations of traditional gas chromatography and high performance liquid chromatography and can realize the qualitative analysis and quantitative detection of strongly polar components. This chromatographic technique features not only simple operations but also rapid analysis. The sensors used in IC are characterized by high sensitivity and selectivity, and the technique can simultaneously separate and determine multiple components. Several advances in IC instrumentation and chromatographic theories have been developed in recent years. IC can analyze various types of samples, including ions, sugars, amino acids, and organic acids (bases). Chinese herbal medicines are typically characterized by highly complex chemical compositions and may contain carbohydrates, proteins, alkaloids, and other active components. They also contain toxic residues such as sulfur dioxide, which may be produced during the processing of medicinal materials. Therefore, the analysis and elucidation of the precise chemical constituents of Chinese herbal medicines present key problems that must be resolved in modern Chinese herbal medicine research. In this context, IC has become an important method for analyzing and identifying the complex components of Chinese herbal medicines because this method is suitable for detecting a single active ingredients among complex components. This paper introduces the different types and principles of IC as well as research progress in this technique. As the applications of IC-based methods in pharmaceutical science, cell biology, and microbiology increase, further development is necessary to expand the applications of this technique. The development of innovative techniques has enabled IC technologies to achieve higher analytical sensitivity, better selectivity, and wider application. The components of Chinese herbal medicines can be divided into endogenous and exogenous components according to their source: endogenous components include glycosides, amino acids, and organic acids, while exogenous components include toxic residues such as sulfur dioxide. Next, the applications of IC to the complex components of Chinese herbal medicines in recent decades are summarized. The most commonly used IC technologies and methods include ion exchange chromatography and conductivity detection. The advantages of IC for the analysis of alkaloids have been demonstrated. This method exhibits better characteristics than traditional analytical methods. However, the applications of IC for the speciation analysis of inorganic anions are limited. Moreover, few reports on the direct application of the technique for the determination of the main active substances in Chinese herbal medicines, including flavonoids, phenylpropanoids, and steroids, have been reported. Finally, this paper reviews new IC technologies and their application progress in Chinese herbal medicine, focusing on their prospects for the effective separation and analysis of complex components. In particular, we discuss the available sample (on-line) pretreatment technologies and explore possible technologies for the selective and efficient enrichment and separation of different components. Next, we assess innovative research on solid-phase materials that can improve the separation effect and analytical sensitivity of IC. We also describe the features of multidimensional chromatography, which combines the advantages of various chromatographic techniques. This review provides a theoretical reference for the further development of IC technology for the analysis of the complex chemical components of Chinese herbal medicines.

An ecological analysis of associations between ambient air pollution and cancer incidence rates in Taiwan.

Air pollution is deemed a human carcinogen and can be linked to certain types of cancer other than lung cancer. The present study aimed to investigate the pollutant-cancer associations in a population-level cohort. We obtained the annual age-standardized incidence rates of 28 different cancer types between 2015 to 2019 from the Taiwan Cancer Registry. Outdoor concentrations of particulate matter with a diameter of 10 µm or less (PM10), sulfur dioxide (SO2), nitrogen dioxide (NO2), ground-level ozone (O3), and carbon monoxide (CO) between 2001 to 2010 were retrieved from the Taiwan Air Quality Monitoring Network. Weighted quantile sum (WQS) regression models were used to determine the combined effects of five air pollutants on the relationship to cancer incidence rates after controlling for sex ratio, age, average disposable income per household, overweight/obesity prevalence, current smoking rate, and drinking rate. Trend analyses showed that NO2 and CO concentrations tended to decrease, while SO2 concentrations increased in some counties. WQS regression analyses revealed significantly positive correlations between air pollutants and liver cancer, lung and tracheal cancer, colorectal cancer, thyroid cancer, kidney cancer, and small intestine cancer. Altogether, the results from this ecological study unravel that exposure to ambient air pollution is associated with the incidence of several non-lung cancer types.

Analysis of emission characteristics associated with vessel activities states in port waters.

This study investigates AIS data from March 2019 to February 2020 in Shanghai Port waters. The ship activities of berthing, waiting, and ingress/egress are extracted in detail to elucidate the emission characteristics of ships in Shanghai Port, differentiating emissions patterns across various ship activity. The findings reveal that the top three pollutants within Shanghai Port are CO2, NOX, and SO2. Container ships have the highest emissions, followed by bulk carriers. Berthed container ships and oil tankers exhibit the highest emission. Waiting container ships emit the most, followed by oil tankers and bulk carriers. Among ships entering and leaving the port, low-speed navigation produces the most emissions, followed by maneuvering and steady-speed navigation. Emission hotspots include the Yangtze River Estuary Anchorage and the main navigation channel into and out of the port. This study offers robust data support for an in-depth understanding and evaluation of ship emission characteristics in Shanghai Port.

Temporal characteristics and health effects related to NO2, O3, and SO2 in an urban area of Iran.

This study reports on temporal variations of NO2, O3, and SO2 pollutants and their related health effects in urban air of Khorramabad, Iran using AirQ 2.2.3 software. Based on data between 2015 and 2021, hourly NO2, O3, and SO2 concentrations increase starting at 6:00 a.m. local time until 9:00 p.m., 3:00 p.m., and 7:00 p.m. local time, respectively, before gradually decreasing. The highest monthly NO2, O3, and SO2 concentrations are observed in October, August, and September, respectively. Annual median NO2, O3, and SO2 concentrations range between 17 ppb and 38.8 ppb, 17.5 ppb-36.6 ppb, and ∼14 ppb-30.8 ppb, respectively. Two to 93 days and 17-156 days between 2015 and 2021 exhibit daily concentrations of NO2 and SO2 ≤ WHO AQGs, respectively, while 187-294 days have 8-h maximum O3 concentrations ≤ WHO AQGs. The mean excess mortality ascribed to respiratory mortality, cardiovascular mortality, hospital admissions for COPD, and acute myocardial infraction are 121, 603, 39, and 145 during 2015-2021, respectively. O3 is found to exert more significant health effects compared to SO2 and NO2, resulting in higher cardiovascular mortality. The gradual increase in NO2 and possibly O3 over the study period is suspected to be due to economic sanctions, while SO2 decreased due to regulatory activity. Sustainable control strategies such as improving fuel quality, promoting public transportation and vehicle retirement, applying subsidies for purchase of electric vehicles, and application of European emission standards on automobiles can help decrease target pollutant levels in ambient air of cities in developing countries.

Indoor sulfur dioxide prediction through air quality modeling and assessment of sulfur dioxide and nitrogen dioxide levels in industrial and non-industrial areas.

In this study, the levels of sulfur dioxide (SO2) and nitrogen dioxide (NO2) were measured indoors and outdoors using passive samplers in Tymar village (20 homes), an industrial area, and Haji Wsu (15 homes), a non-industrial region, in the summer and the winter seasons. In comparison to Haji Wsu village, the results showed that Tymar village had higher and more significant mean SO2 and NO2 concentrations indoors and outdoors throughout both the summer and winter seasons. The mean outdoor concentration of SO2 was the highest in summer, while the mean indoor NO2 concentration was the highest in winter in both areas. The ratio of NO2 indoors to outdoors was larger than one throughout the winter at both sites. Additionally, the performance of machine learning (ML) approaches: multiple linear regression (MLR), artificial neural network (ANN), and random forest (RF) were compared in predicting indoor SO2 concentrations in both the industrial and non-industrial areas. Factor analysis (FA) was conducted on different indoor and outdoor meteorological and air quality parameters, and the resulting factors were employed as inputs to train the models. Cross-validation was applied to ensure reliable and robust model evaluation. RF showed the best predictive ability in the prediction of indoor SO2 for the training set (RMSE = 2.108, MAE = 1.780, and R2 = 0.956) and for the unseen test set (RMSE = 4.469, MAE = 3.728, and R2 = 0.779) values compared to other studied models. As a result, it was observed that the RF model could successfully approach the nonlinear relationship between indoor SO2 and input parameters and provide valuable insights to reduce exposure to this harmful pollutant.

A highly sensitive ratiometric fluorescent probe for detecting HSO3-/SO32- and viscosity change based on FRET/TICT mechanism.

BACKGROUND: Sulfur dioxide (SO2) is a significant gas signaling molecule in organisms, and viscosity is a crucial parameter of the cellular microenvironment. They are both involved in regulating many physiological processes in the human body. However, abnormalities in SO2 and viscosity levels are associated with various diseases, such as cardiovascular disease, lung cancer, respiratory diseases, neurological disorders, diabetes and Alzheimer's disease. Hence, it is essential to explore novel and efficient fluorescent probes for simultaneously monitoring SO2 and viscosity in organisms. RESULTS: We selected quinolinium salt with good stability, high fluorescence intensity, good solubility and low cytotoxicity as the fluorophore and developed a highly sensitive ratiometric probe QQD to identify SO2 and viscosity changes based on Förster resonance energy transfer/twisted intramolecular charge transfer (FRET/TICT) mechanism. Excitingly, compared with other probes for SO2 detection, QQD not only identified HSO3-/SO32- with a large Stokes shift (218 nm), low detection limit (1.87 µM), good selectivity, high energy transfer efficiency (92 %) and wide recognition range (1.87-200 µM), but also identified viscosity with a 26-fold fluorescence enhancement and good linearity. Crucially, QQD was applied to detect HSO3-/SO32- and viscosity in actual water and food samples. In addition, QQD had low toxicity and good photostability for imaging HSO3-/SO32- and viscosity in cells. These results confirmed the feasibility and reliability of QQD for HSO3-/SO32- and viscosity imaging and environmental detection. SIGNIFICANCE: We reported a unique ratiometric probe QQD for detecting HSO3-/SO32- and viscosity based on the quinolinium skeleton. In addition to detecting HSO3-/SO32- and viscosity change in actual water and food samples, QQD could also monitor the variations of HSO3-/SO32- and viscosity in cells, which provided an experimental basis for further exploration of the role of SO2 derivatives and viscosity in biological systems.

Effects of post-fermentation addition of green tea extract for sulfur dioxide replacement on Sauvignon Blanc wine phenolic composition, antioxidant capacity, colour, and mouthfeel attributes.

This study examines the feasibility of replacing SO2 in a New Zealand Sauvignon Blanc wine with a green tea extract. The treatments included the control with no preservatives (C), the addition of green tea extract at 0.1 and 0.2 g/L (T1 and T2), and an SO2 treatment at 50 mg/L (T3). Five monomeric phenolic compounds were detected in the green tea extract used for the experiment, and their concentrations ranged in the order (-)-epigallocatechin gallate > (-)-epigallocatechin > (-)-epicatechin > (-)-epicatechin gallate > gallic acid. At the studied addition rates, these green tea-derived phenolic compounds contributed to ∼70% of the antioxidant capacity (ABTS), ∼71% of the total phenolic index (TPI), and âˆ¼ 84% of tannin concentration (MCPT) of the extract dissolved in a model wine solution. Among wine treatments, T1 and T2 significantly increased the wine's colour absorbance at 420 nm, MCPT, gallic acid and total monomeric phenolic content. TPI and ABTS were significantly higher in wines with preservatives (i.e., T2 > T1 â‰… T3 > C, p < 0.05). These variations were observed both two weeks after the treatments and again after five months of wine aging. Additionally, an accelerated browning test and a quantitative sensory analysis of wine colour and mouthfeel attributes were performed after 5 months of wine aging. When exposed to excessive oxygen and high temperature (50 °C), T1 and T2 exhibited ∼29% and 24% higher browning capacity than the control, whereas T3 reduced the wine's browning capacity by ∼20%. Nonetheless, the results from sensory analysis did not show significant variations between the treatments. Thus, using green tea extract to replace SO2 at wine bottling appears to be a viable option, without inducing a negative impact on the perceptible colour and mouthfeel attributes of Sauvignon Blanc wine.

Effect of SO2, glutathione, and glutathione-rich inactive dry yeast on the color, phenolic compounds, ascorbic acid, and antioxidant activity of Roxburgh rose wine.

Conventional methods for inhibiting browning in wine are not suitable for cili (Roxburgh rose) wine, which is naturally rich in ascorbic acid and subject to restrictions on SO2 addition. In this study, the capacity of various additives to suppress the browning of cili wine caused by ascorbic acid degradation was investigated. SO2, pure reduced glutathione (GSH), regular inactive dry yeast (IDY), and IDY with various levels of glutathione enrichment (g-IDY) were separately introduced into cili wine following the completion of alcoholic fermentation. Over a period of 12 months, the color parameters, levels of ascorbic acid, phenolic compounds, antioxidant activity, and GSH content of the aged cili wine were analyzed. Among the investigated additives, g-IDY exhibited the strongest inhibitory effect on browning. Moreover, adding 800 mg L-1 g-IDY increased the total reducing power and residual GSH content by factors of 1.52 and 2.44, respectively, with respect to those of the SO2-treated cili wine, thus enhancing its antioxidant capacity. Using ultra-performance liquid chromatography-tandem mass spectrometry analysis, a total of 22 monomeric phenolic compounds were identified. After g-IDY treatment, the contents of 15 easily oxidizable o-diphenols decreased, preventing the depletion of ascorbic acid as an antioxidant. As a result, the levels of ascorbic acid and total phenolics were 1.5-fold and 1.17-fold higher than those in the SO2-treated wine, respectively. This study demonstrates that g-IDY provides a new approach to preventing the browning of wine caused by ascorbic acid degradation. PRACTICAL APPLICATION: Cili wine, characterized by its high ascorbic acid content, can decelerate cellular senescence and bolster immune function, which has contributed to its popularity. Ascorbic acid, a potent antioxidant, can be spiked into white wines to significantly enhance their antioxidative properties. Nevertheless, the high ascorbic acid content in cili wine renders it susceptible to oxidation under both aerobic and anaerobic conditions, which alters the wine's hue from golden to dark brown, thus diminishing its commercial value. Overcoming this browning associated with ascorbic acid degradation is therefore of considerable importance and could facilitate the advancement of the cili industry.

Air pollution and upper respiratory diseases: an examination among medically insured populations in Wuhan, China.

Multiple evidence has supported that air pollution exposure has detrimental effects on the cardiovascular and respiratory systems. However, most investigations focus on the general population, with limited research conducted on medically insured populations. To address this gap, the current research was designed to examine the acute effects of inhalable particulate matter (PM2.5 and PM10), nitrogen dioxide (NO2), ground-level ozone (O3), and sulfur dioxide (SO2) on the incidence of upper respiratory tract infections (URTI), utilizing medical insurance data in Wuhan, China. Data on URTI were collected from the China Medical Insurance Basic Database for Wuhan covering the period from 2014 to 2018, while air pollutant data was gathered from ten national monitoring stations situated in Wuhan city. Statistical analysis was performed using generalized additive models for quasi-Poisson distribution with a log link function. The analysis indicated that except for ozone, higher exposure to four other pollutants (NO2, SO2, PM2.5, and PM10) were significantly linked to an elevated risk of URTI, particularly during the previous 0-3 days and previous 0-4 days. Additionally, NO2 and SO2 were found to be positively linked with laryngitis. Furthermore, the effects of air pollutants on the risk of URTI were more pronounced during cold seasons than hot seasons. Notably, females and the employed population were more susceptible to infection than males and non-employed individuals. Our findings gave solid proof of the link between ambient air pollution exposure and the risk of URTI in medically insured populations.

The association between ambient air pollution and migraine: a systematic review.

Some studies have shown the effect of air pollution on migraine. However, it needs to be confirmed in larger-scale studies, as scientific evidence is scarce regarding the association between air pollution and migraine. Therefore, this systematic review aims to determine whether there are associations between outdoor air pollution and migraine. A literature search was performed in Scopus, Medline (via PubMed), EMBASE, and Web of Science. A manual search for resources and related references was also conducted to complete the search. All observational studies investigating the association between ambient air pollution and migraine, with inclusion criteria, were entered into the review. Fourteen out of 1417 identified articles met the inclusion criteria and entered the study. Among the gaseous air pollutants, there was a correlation between exposure to nitrogen dioxide (NO2) (78.3% of detrimental relationships) and carbon monoxide (CO) (68.0% of detrimental relationships) and migraine, but no apparent correlation has been found for sulfur dioxide (SO2) (21.2% of detrimental relationships) and ozone (O3) (55.2% of detrimental relationships). In the case of particulate air pollutants, particulate matter with a diameter of 10 µm or less (PM10) (76.0% of detrimental relationships) and particulate matter with a diameter of 2.5 µm or less (PM2.5) (61.3% of detrimental relationships) had relationships with migraine. In conclusion, exposure to NO2, CO, PM10, and PM2.5 is associated with migraine headaches, while no conclusive evidence was found to confirm the correlation between O3 and SO2 with migraine. Further studies with precise methodology are recommended in different cities around the world for all pollutants with an emphasis on O3 and SO2.

The effects of increasing emission Fee rates on Chinese firms' emissions: Evidence from a quasi-experiment.

Over the last four decades, the Chinese government has predominantly employed emission fees as a regulatory strategy to mitigate pollution from firms. However, the effectiveness of escalating emission fee rates on the emission levels of Chinese firms has not been examined. This study utilizes data from more than 80,000 Chinese firms spanning 2004-2013, employing difference-in-differences models to assess the effects of rising emission fee rates on firm emissions. The findings indicate the following: (1) Increased emission fee rates substantially reduce sulfur dioxide and chemical oxygen demand emissions among Chinese firms; (2) These heightened fees encourage firms to implement both end-of-pipe treatment and source control for sulfur dioxide and end-of-pipe treatment for chemical oxygen demand; (3) The emission reduction effects vary according to firm ownership and size. This research offers empirical evidence on the efficacy of emission fee systems and provides valuable insights for developing market incentive-based environmental regulations in the future.

Pollutant reduction effects of vertical environmental reform in China.

The vertical environmental reform in China has led to the change of environmental management system from territorial management model to vertical management model. This study uses the data of 263 prefecture-level cities in China to examine the effects of China's vertical environmental reform on pollutant emissions, including industrial sulfur dioxide, wastewater emissions, and industrial fumes emissions. The findings demonstrate that vertical environmental reform resulted in a reduction in industrial sulfur dioxide, wastewater emissions, and industrial fumes emissions. And the governance effects is gradually enhanced with the passage of time, which are long-lasting effects. The above conclusions are still valid after a series of robust estimates including mitigating selection bias, placebo test, changing the dependent variables, and mitigating heterogeneous treatment effects. According to heterogeneity analysis, the vertical environmental reform has reduced the increase of pollutants caused by financial pressure and official associations, and treats border pollution problems more effectively. Under the decentralized governance system, the implementation of vertical environmental management helps to reduce local pollutant emissions. This conclusion provides the latest evidence from China for the academic debate on the advantages and disadvantages of territorial environmental management and vertical environmental management and also provides policy implications for the government's environmental governance.

Artificial intelligence-based forecasting model for incinerator in sulfur recovery units to predict SO2 emissions.

Pollutant emissions from chemical plants are a major concern in the context of environmental safety. A reliable emission forecasting model can provide important information for optimizing the process and improving the environmental performance. In this work, forecasting models are developed for the prediction of SO2 emission from a Sulfur Recovery Unit (SRU). Since SRUs incorporate complex chemical reactions, first-principle models are not suitable to predict emission levels based on a given feed condition. Accordingly, artificial intelligence-based models such as standard machine learning (ML) algorithms, multi-layer perceptron (MLP), long short-term memory (LSTM), one-dimensional convolution (1D-CNN), and CNN-LSTM models were tested, and their performance was evaluated. The input features and hyperparameters of the models were optimized to achieve maximum performance. The performance was evaluated in terms of mean squared error (MSE) and mean absolute percentage Error (MAPE) for 1 h, 3 h and 5 h ahead of forecasting. The reported results show that the CNN-LSTM encoder-decoder model outperforms other tested models, with its superiority becoming more pronounced as the forecasting horizon increased from 1 h to 5 h. For the 5-h ahead forecasting, the proposed model showed a MAPE advantage of 17.23%, 4.41%, and 2.83%, respectively over the 1D-CNN, Deep LSTM, and single-layer LSTM models in the larger dataset.