Knowledge, attitude, and practice toward postoperative self-management among kidney transplant recipients.

BACKGROUND: Patient involvement is crucial to the success of kidney transplants. This study aimed to investigate the knowledge, attitude, and practice (KAP) toward postoperative self-management among kidney transplant recipients. METHODS: A web-based cross-sectional study was conducted in Ruijin Hospital (Shanghai, China) between March 24, 2023, and April 15, 2023 in kidney transplant recipients. A questionnaire was designed to collect data about the characteristics of the participants and their KAP toward postoperative self-management. KAP scores were calculated based on participants' responses, using predefined scoring criteria tailored to evaluate each dimension of KAP effectively. RESULTS: A total of 483 valid questionnaires were collected, including 189 (39.13%) participants aged between 46 and 60 years. The mean score of knowledge, attitude and practice were 23.44 ± 4.87 (possible range: 0-28), 43.59 ± 2.65 (possible range: 10-50), 52.52 ± 4.64 (possible range: 0-58), respectively. The multivariate analysis showed knowledge scores (OR = 1.15, 95% CI = 1.10-1.20, p < 0.001), attitude scores (OR = 1.22, 95% CI = 1.12-1.32, p < 0.001) and undergone transplantation within 1 year (OR = 3.92, 95% CI = 1.60-9.63, p = 0.003) were independently associated with good practice. Knowledge scores (OR = 1.06, 95% CI = 1.02-1.10, p = 0.003), attitude scores (OR = 1.16, 95% CI = 1.08-1.25, p < 0.001), aged 16-35 years (OR = 0.38, 95% CI = 0.18-0.78, p = 0.009), underwent a single kidney transplant surgery (OR = 3.97, 95% CI = 1.28-12.38, p = 0.017) were independently associated with medication adherence. CONCLUSIONS: Kidney transplant recipients had good knowledge, positive attitude and good practice toward postoperative self-management. Implementing personalized education, psychological support, and close monitoring strategies is recommended to optimize postoperative self-management in kidney transplant recipients.

Impact of VOCs emission from iron and steel industry on regional O3 and PM2.5 pollutions.

Iron and steel industry emission is an important industrial source of air pollution. However, little is known about the relationship between volatile organic compounds (VOCs) emitted and regional air pollution. In this study, VOCs emissions from a typical iron and steel plant in Yangtze River Delta (YRD, China) were monitored from April 2018 to March 2019. The ozone formation potential (OFP) and secondary organic aerosol (SOA) formation of VOCs were calculated to reveal the influence of VOCs emissions on regional ozone and particulate pollution, and the sensitivity analysis approach was performed to explore the qualitative and quantitative relationships between VOCs and O3, as well as VOCs and PM2.5. The VOCs concentration was 93.76 ± 266.97 ppbv during the study. The OFP was 760.08 ± 2391.90 µg m-3, and aromatics were the predominant precursors, contributing 54.05% of the total OFP. Furthermore, the SOA estimated by fractional aerosol coefficient (FAC) and time-resolved (TR) methods were 6.032 ± 13.347 µg m-3 and 0.971 ± 4.650 µg m-3, accounting for 8.65-26.39% (13.78 ± 7.46%) and 1.55-4.20% (2.22 ± 1.23%) of the PM2.5 concentrations, respectively. The results indicated that VOCs were more sensitive to O3 pollution in high pollution domains, whereas VOCs were more sensitive to PM2.5 pollution in low pollution domains. We concluded that reducing VOCs emissions might be effective in alleviating photochemical pollution episodes in areas around iron and steel industry, and the haze pollution occurred in these regions may be caused by the primary emission of PM, and the contribution of SOA was relatively small.

Characteristics and health risk assessment of volatile organic compounds (VOCs) in restaurants in Shanghai.

Volatile organic compounds (VOCs) are important precursors of ozone and atmospheric particulates that have attracted extensive attention worldwide. Cooking emissions, the chemical characteristics of which vary dramatically due to different cooking styles, are a main source of ambient VOCs, especially in large cities. This research focused on the emission characteristics of VOCs from six types of restaurants in Shanghai: hot pot (HP), Sichuan cuisine (SC), Cantonese cuisine (CS), seafood (SF), Western fast food (WFF), and authentic Shanghai cuisine (ASC). It was found that HP, which discharged cooking fumes indoors, produced the highest mass concentration of VOCs (1900.2 ± 364.8 µg m-3), followed by SC (1403.7 ± 403.8 µg m-3), WFF (656.0 ± 156.9 µg m-3), SF (638.6 ± 145.1 µg m-3), CC (632.7 ± 127.7 µg m-3), and ASC (612.3 ± 51.3 µg m-3), the cooking fumes from which were collected by emission extraction stacks. Additionally, the VOC species from each cuisine were mainly low carbon substances. Alkanes were the major VOC pollutants from all six cuisines, accounting for 34.4-71.7%. The coefficient divergence values were 0.287-0.593, suggesting that there were differences between the cuisines in the present study. Ozone formation potential and secondary organic aerosol formation potential indicated that O-VOCs and aromatics were the largest contributors. Health risk assessment of VOCs via non-carcinogenic risk values (HQ) and carcinogenic risk values (RISK) indicated that frying, grilling, and stir-frying had relatively large impacts on human health. VOCs collected in emission extraction stacks were significantly higher risk compared with those in the indoor environment, but the RISK score of the HP restaurant was larger, second only to SC. The HQ and RISK values of 1,3-butadiene, acetaldehyde, and trichloroethylene in the HP restaurant all exceeded US EPA standards, indicating that long-term exposure in an HP restaurant would have a significant impact on human health and might carry a potential cancer risk.

Emission characteristics of fine particulate matter from ultra-low emission power plants.

As one of the highest energy consuming and polluting industries, the power generation industry is an important source of particulate matter emissions. Recently, implementation of ultra-low emission technology has changed the emission characteristic of fine particulate matter (PM2.5). In this study, PM2.5 emitted from four typical power plants in China was sampled using a dilution channel sampling system, and analyzed for elements, water-soluble ions and carbonaceous fractions. The results showed that PM2.5 concentrations emitted from the four power plants were 0.78 ±â€¯0.16, 0.63 ±â€¯0.09, 0.29 ±â€¯0.07 and 0.28 ±â€¯0.01 mg m-3, respectively. Emission factors were 0.004-0.005 g/kg coal, nearly 1-2 orders of magnitude lower than those reported in previous studies. The highest proportions of PM2.5 consisted of organic carbon (OC), SO42-, elemental carbon (EC), NH4+, Al and Cl-. Coefficients of divergence (CDs) were in the ranges 0.22-0.41 (for an individual plant), 0.43-0.69 (among different plants), and 0.60-0.99 (in previous studies). The results indicated that the source profiles of each tested power plant were relatively similar, but differed from those in previous studies. Enrichment factors showed elevated Se and Hg, in accordance with the source markers Se and As. Comparing source profiles with previous studies, the proportion of OC, EC and NH4+ were higher, while the proportion of Al in PM2.5 were relatively lower. The OC/EC ratio became concentrated at ∼5. Results from this study can be used for source apportionment and emission inventory calculations after implementation of ultra-low emission technologies.

A high temporal-spatial emission inventory and updated emission factors for coal-fired power plants in Shanghai, China.

With the implementation of the ultra-low emission policy in China, emission factors (EFs) of power plant pollutants are constantly changing. Emission inventories developed using the recommended EFs contain high levels of uncertainty and it is difficult to achieve a high temporal resolution. Detailed sulfur dioxide (SO2), nitrogen oxides (NOx), and particulate matter (PM) emission data based on a continuous emission monitoring system (CEMS) were obtained from 33 units at 13 power plants in Shanghai in 2017. The data were used to develop an hourly unit-based emission inventory and to devise updated EFs for coal-fired power plants. Emissions of SO2, NOx, and PM typically met the ultra-low emission limit, with total emissions of SO2, NOx, and PM of 2895.0, 5348.3, and 503.8 tons, respectively. Emission proportions of SO2 and NOx for 300-600, 600-1000, and above 1000 MW units were similar, while the emission proportion of PM decreased with an increase in unit capacity. Emissions of SO2, NOx, and PM displayed similar monthly variations, peaking in winter and summer. Diurnal hourly variations of SO2, NOx, and PM emissions displayed a bimodal trend, with higher emissions at night on weekends than on weekdays. EFs based on CEMS (EFC) of SO2, NOx, and PM were 0.10, 0.36, and 0.04 g kg-1 of coal, respectively, which were one or two orders of magnitude lower than the widely-used EFs and 4-30 times lower than EFs based on the mass balance approach. After replacing the recommended fixed decontamination efficiencies with individually fitted values, the calculated EFs were consistent with the corresponding EFC and discrepancies were further reduced. The new inventory and updated EFs will enable a better understanding of the temporal variations of power plant emissions and reduce the uncertainty caused by the overestimation of EFs after the implementation of ultra-low emissions technology.

Variation of wastewater contaminants in China and source identification using hierarchical clustering based on weighted factor scores.

Human activities have huge impact on the aquatic environment. Knowledge on sources of the contaminants provides guidelines to determine the ideal location and maintenance of monitoring stations, thus advancing environmental monitoring and pollution control. Factor analysis (FA) may be the most popular method for source identification, but the results should be affirmed. Following this logic, in this research, firstly the potential sources were determined, and secondly the contaminant concentrations in the source regions and the non-source regions were compared. To identify the potential sources, 75 meteorological, economic and social indicators were used to group the study regions. FA was used to reduce dimensionality and factor scores were calculated. The grouping was based on the weighted factor scores while the weight was variance explained by each factor respectively. Each group was supposed to correspond to a factor; that is, a potential source. The results indicated that the concentrations of chemical oxygen demand (COD), ammonia nitrogen, phosphorus and arsenic in wastewater were significantly different between groups. Animal husbandry, mining and/or energy industry were the main sources of COD, ammonia nitrogen and phosphorus; animal husbandry, mining, energy industry, and/or heavy and chemical industry were the main sources of phosphorus; humid climate and/or secondary industry were the main sources of arsenic.

Investigate the impact of local iron-steel industrial emission on atmospheric mercury concentration in Yangtze River Delta, China.

Mercury is a global neurotoxic pollutant, which can be globally transported and bioaccumulated in the food chain. Iron-steel production is one of the most significant sources of anthropogenic atmospheric mercury emission, while information on this source is scarce. Hourly gaseous elemental mercury (GEM) and particle bound mercury (PBM) were studied inside (IP) and at the boundary (BP) of a typical iron-steel plant in the Yangtze River Delta (YRD), China from September 2016 to August 2017. The GEM concentrations were 0.97-503.1 and 0.05-112.6 ng/m3 at the IP and BP sites, respectively, while PBM concentrations were one to four orders of magnitude higher than urban and suburban ambient levels. Several lines of evidences indicated that PBM was mainly originated from the iron-steel manufacturing process, especially from sintering and coke-making processes in this iron-steel plant. However, a combined emission effect contributed to GEM variation. The receptor model of positive matrix factorization (PMF) showed that local direct emissions (coal combustion, industrial activity, vehicle exhaust, and secondary evaporation from polluted soil) contributed 51.3% of the total GEM concentration variation. Potential source contribution function (PSCF) and concentration weighted trajectory (CWT) models clearly showed that air masses moving from areas surrounding YRD had the highest concentrations of atmospheric mercury. These results provided evidence that iron-steel manufacturing emissions have a considerable effect on regional atmospheric mercury concentrations, especially PBM.