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Background: Cooking and fuel combustion in the indoor environment are major sources of respirable suspended particulate matter (RSPM), which is an excellent carrier of potentially harmful absorbed inorganic and organic compounds. Chronic exposure to RSPM can lead to acute pulmonary illness, asthma, cardiovascular disease, and lung cancer in people involved in cooking. Despite this, questions remain about the harmfulness of different particulate matter (PM) sources generated during cooking, and the factors influencing PM physico-chemical properties. The most reliable methods for sampling and analyzing cooking emissions remain only partially understood. Objectives: This review aims to comprehensively assess the risks of PM generated during cooking, considering the main sources of PM, PM chemical composition, and strategies for PM physico-chemical analysis. We present the first systematic analysis of PM sources and chemical composition related to cooking. We highlight significant differences between studies using different experimental conditions, with a lack of a standard methodology. Methods: Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement rules and the Patient, Intervention, Comparison, and Outcome (PICO) strategy for scientific research, three different scientific databases (PubMed, Scopus, and Web of Science) were screened to find scientific articles that measure, collect, and analyze the chemical composition of nanometer- and micrometer-sized PM generated during cooking activities under different conditions. Data are summarized to assess risk, evaluating the main sources and factors influencing PM generation, their chemical composition, and how they have been collected and analyzed in changing experimental conditions. Results: From 2474 search results, there were 55 studies that met our criteria. Overall, the main variable sources of PM in cooking activities relate to the stove and fuel type. The concentration and chemical–physical properties of PM are also strongly influenced by the food and food additive type, food processing type, cooking duration, temperature, and utensils. The most important factor influencing indoor PM concentration is ventilation. The PM generated during cooking activities is composed mainly of elemental carbon (EC) and its derivatives, and the porous structure of PM with high surface-to-volume ratio is a perfect carrier of inorganic and organic matter. Conclusions: This review reveals a growing interest in PM exposure during cooking activities and highlights significant variability in the chemical–physical properties of particles, and thus variable exposure risks. Precise risk characterization improves possible preventive strategies to reduce the risk of indoor pollutant exposure. However, comprehensive PM analysis needs proper sampling and analysis methods which consider all factors influencing the physico-chemical properties of PM in an additive and synergistic way. Our analysis highlights the need for method standardization in PM environmental analyses, to ensure accuracy and allow deeper comparisons between future studies.

Official air quality (AQ) stations are sporadically located in cities to monitor the anthropogenic pollutant levels. Consequently, their data cannot be used for further locations to estimate hidden changes in AQ and local emissions. Low-cost sensors (LCSs) of particulate matter (PM) in a network can help in solving this problem. However, the applicability of LCSs in terms of analytical performance requires careful evaluation. In this study, two types of pocket-size LCSs were tested at urban, suburban and background sites in Budapest, Hungary, to monitor PM1, PM2.5, PM10, and microclimatic parameters at high resolutions (1 s to 5 min). These devices utilize the method of laser irradiation and multi-angle light scattering on air-suspended particulates. A research-grade AQ monitor was applied as a reference. The LCSs showed acceptable accuracy for PM species in indoor/outdoor air even without calibration. Low PM readings (<10 μg/m3) were generally handicapped by higher bias, even between sensors of the same type. The relative humidity (RH) slightly affected the PM readings of LCSs at RHs higher than 85%, necessitating field calibration. The air quality index was calculated to classify the extent of air pollution and to make predictions for human health effects. The LCSs were useful for detecting peaks stemming from emissions of motor vehicular traffic and residential cooking/heating activities.

Yue, Y.L.; Qing, S.; Diao, R.X., and Hao, Y.L., 2020. Remote sensing of suspended particulate matter in optically complex estuarine and inland waters based on optical classification. In: Jung, H.-S.; Lee, S.; Ryu, J.-H., and Cui, T. (eds.), Advances in Geospatial Research of Coastal Environments. Journal of Coastal Research, Special Issue No. 102, pp. 303-317. Coconut Creek (Florida), ISSN 0749-0208. Accurate suspended particulate matter (SPM) concentration retrieval across complex estuarine to inland waters from ocean color remote sensing reflectance (Rrs(λ)) faces challenges. In this paper, an optical classification-based SPM retrieval algorithm in optically complex estuarine and inland waters was proposed and tested in the Yellow River Estuary and Daihai Lake, China. Firstly, the in situ measured Rrs(λ) (n = 204) were classified into two optical water types with the method defined by Matsushita et al. (2015). Secondly, we designed several mathematical models and selected the optimal algorithm according to the goodness of fit. Optimal algorithms were developed for each water type to achieve accurate SPM retrieval. Through the construction of the optimal retrieval algorithm in each water type, the uncertainty of SPM retrievals has been reduced from 95 % to about 39 % compared with the algorithm without optical classification. The retrieval algorithm based on optical water classification was further applied to the Sentinel-2 MSI L2A data over the study area and produced reliable SPM maps. Independent validation with the in situ-satellite match-ups further demonstrates the algorithm's validity (uncertainty of about 47 %). In contrast, applications of other SPM retrieval algorithms resulted in less reliable SPM results with either unsatisfactory retrieval accuracy in class1 (the lowest value of r can reach 0.02). The optical classification, together with the optimal retrieval algorithm for each optical type, is proved to be a feasible way for SPM retrieval in high accuracy over optically complex waters.

Oil exploitation, the basis of the world energy sector, is linked to risks and accidents, causing damage to the affected regions. Oil-suspended particulate matter aggregate (OSA) is a promising technology to mitigate those effects. The present study periodically (February 2016 and July 2016) evaluated the dispersion of oil at 28 points in the São Paulo River’s estuary, Todos os Santos Bay, Brazil, analyzing the influence of suspended particulate matter (SPM), particulate organic carbon (POC), ions, and chlorophyll on the formation OSA, targeting the prediction of possible ecotoxicological risks. The results showed that the estuary presented similar characteristics in the expeditions, reflecting the oil dispersion pattern through the formation of OSAs, being 92.86% dispersed in the column in the first and 85.71% in the second expedition. The results also pointed to the possibility of pollution in the food chain, reduced fertility, the emergence of abnormalities and the gradual disappearance of species across the whole river in a possible oil spill.

In this article, a comprehensive literature survey on air pollution and its effects on the human respiratory system is carried out. Based on the knowledge gaps, a computational assessment is proposed to find the impact of air quality on respiratory suspended particulate matter (RSPM) deposition in the human airways. A realistic 3D geometric model of the human airway was constructed to study the airflow characteristics and RSPM (PM2.5 and PM10) transport and deposition in it for normal and moderate inhalation patterns (corresponding to natural breathing) of air having an unhealthy air quality index (AQI). The results identify inertial impact as the primary mechanism of particle deposition in the human airways. They also reveals the significant differences in the deposition patterns of PM2.5 and PM10 in the right and left bronchial airways.

Per- and polyfluoroalkyl substances (PFASs) are bioaccumulative and widely distributed persistent organic pollutants (POPs). Understanding the distribution of and ecological risks posed by PFASs is critical, particularly for PFAS emissions and accumulation from a common urban pollution source. The transformation characteristics and ecological risks of PFASs from a typical aging municipal landfill leachate were systematically monitored and assessed over five years in this study. The results showed that the total PFAS concentrations (ΣPFASs) in the rivers were between 26.4 and 464.3 ng/L, whereas in sediment, ΣPFASs ranged from 9.5 to 58.5 ng/g (w/w). The presence of perfluorooctanoic acid (PFOA) was the most prominent PFAS in both water (39.4–152.3 ng/L) and sediment (1.1–56.1 ng/g). In a five-year monitoring study, the concentration of PFASs in the aging landfill decreased by 23.3%, with higher mean concentrations observed during summer (307.6 ng/L) compared to winter (250.4 ng/L). As for the pollution distribution, the suspended particulate matter–water partition coefficient (log Kd) of carboxylic acid (PFCAs) and perfluoroalkane sulfonic acids (PFSAs) ranged from 1.53 to 2.65, and from 1.77 to 2.82, respectively. PFSAs and long-chain PFCAs exhibited a greater propensity for sediment association compared to short-chain PFCAs. An ecological risk assessment of four typical PFASs, PFOA, perfluorooctane sulfonate (PFOS), perfluorobutanoic acid (PFBA), and perfluorobutane sulfonic acid (PFBS), utilizing the hazard quotient method revealed that the rivers surrounding the typical aging landfill exhibited a low contamination risk for PFOA, while no ecological risks were associated with the other three FPASs. This study contributes to an enhanced comprehension of the occurrence, distribution, and risk of PFASs in the rivers in rivers and sediment surrounding a typical aging landfill site in China, thereby providing crucial reference information for ensuring water quality safety.

The present study examines the effect of Diwali festival (17–21 October 2017; 19th October was the Diwali day) on aerosol characteristics over Patiala, northwestern part of India. Diwali being one of the major festivals of India that falls between mid-October and mid-November is celebrated with full enthusiasm by burning crackers, fireworks, etc. During this period, the study site also is engulfed with high aerosol loading because of extensive paddy residue burning emission. During Diwali event, a particulate matter (PM 10 ) concentration varies from 132 to 155 μg m −3 , while a mass concentration of black carbon aerosols varies from 6 to 9 μg m −3 with the maximum concentration on post-Diwali day. Aerosol optical depth (AOD 500 ) was maximum (0.852) on post-Diwali day indicating the additional loading of submicron particles due to burning of crackers and fireworks. The magnitude of single scattering albedo (SSA 500 ) decreases to a minimum value around 0.864 showing abundance of absorbing aerosols on Diwali affected days (19th and 20th October). A sudden jump of +12.9 W m −2 in atmospheric radiative forcing resulting in a heating rate of up to 1.4 K day −1 on next day of Diwali shows the warming state of the lower and middle atmosphere.

Transitional ecosystems, estuaries and the coastal seas, are distinctively affected by natural and anthropogenic factors. Organic matter (OM) originating from terrestrial sources is exported by rivers and forms a key component of the global biogeochemical cycles. Most previous studies focused on the bulk biochemical and anthropogenic aspects affecting these ecosystems. In the present study, we examined the sources and fate of OM entrained within suspended particulate matter (SPM) of the Zuari River and its estuary, west coast of India. Besides using amino acid (AA) enantiomers (L- and D-forms) as biomarkers, other bulk biochemical parameters viz. particulate organic carbon (POC), δ 13 C, particulate nitrogen (PN), δ 15 N and chlorophyll a were analyzed. Surprisingly no significant temporal variations were observed in the parameters analyzed; nonetheless, salinity, POC, δ 13 C, PN, δ 15 N, glutamic acid, serine, alanine, tyrosine, leucine and D-aspartic acid exhibited significant spatial variability suggesting source differentiation. The POC content displayed weak temporal variability with low values observed during the post-monsoon season attributed to inputs from mixed sources. Estuarine samples were less depleted than the riverine samples suggesting contributions from marine plankton in addition to contributions from river plankton and terrestrial C 3 plants detritus. Labile OM was observed during the monsoon and post-monsoon seasons in the estuarine region. More degraded OM was noticed during the pre-monsoon season. Principal component analysis was used to ascertain the sources and factors influencing OM. Principally five factors were extracted explaining 84.52% of the total variance. The first component accounted for 27.10% of the variance suggesting the dominance of tidal influence whereas, the second component accounted for heterotrophic bacteria and their remnants associated with the particulate matter, contributing primarily to the AA pool. Based on this study we ascertained the role of the estuarine turbidity maximum (ETM) controlling the sources of POM and its implications to small tropical rivers. Thus, changes in temporal and regional settings are more likely to affect the natural biogeochemical cycles of small tropical rivers.

Fine particulate matter (PM2.5) decreased by 30–40% across China during 2013–2017 in response to the governmental Clean Air Action. However, surface ozone pollution worsened over the same period. Model simulations have suggested that the increase in ozone could be driven by the decrease in PM2.5, because PM2.5 scavenges hydroperoxy (HO2) and NOx radicals that would otherwise produce ozone. Here we show observational evidence for this effect with 2013–2018 summer data of hourly ozone and PM2.5 concentrations from 106 sites in the North China Plain. The observations show suppression of ozone pollution at high PM2.5 concentrations, consistent with a model simulation in which PM2.5 scavenging of HO2 and NOx depresses ozone concentrations by 25 ppb relative to PM2.5-free conditions. PM2.5 chemistry makes ozone pollution less sensitive to NOx emission controls, emphasizing the need for controlling emissions of volatile organic compounds (VOCs), which so far have not decreased in China. The new 2018–2020 Clean Air Action plan calls for a 10% decrease in VOC emissions that should begin to reverse the long-term ozone increase even as PM2.5 continues to decrease. Aggressive reduction of NOx and aromatic VOC emissions should be particularly effective for decreasing both PM2.5 and ozone.

Split window (SW) methods, which have been successfully used to retrieve measurements of land surface temperature (LST) and sea surface temperature (SST) from MODIS images, were exploited to evaluate the SST data of three sections of Italian coastal waters. For this purpose, sea surface emissivity (SSE) values were estimated by adding the effects of salinity and total suspended particulate matter (SPM) concentrations, sea surface wind speed, and zenith observation angle. The total column atmospheric water vapor contents were retrieved from MODIS data. SST data retrieved from MODIS images using these algorithms were compared with SSTskin measurements evaluated from in situ data. The comparison showed that the algorithms for retrieving LST measurements minimized the error in SST data in near-land coastal waters with respect to the algorithms for retrieving SST measurements: a method for retrieving LST measurements highlighted the smallest root-mean-square deviation (RMSD) value (0.48 K) and values of maximum bias and standard deviation (σ) equal to −3.45 K and 0.41 K; the current operation algorithm for retrieving LST data highlighted the smallest values of maximum bias and σ (−1.37 K and 0.35 K) and an RMSD value of 0.66 K; and the current operation algorithm for retrieving global measurements of SST showed values of RMSD, maximum bias, and σ equal to 0.68 K, −1.90 K, and 0.40 K, respectively.