Concerns over regional climate change include its impact on air quality. A major contributor to unhealthy air quality is surface-based temperature inversions. Poor air quality is a serious public health concern that is often addressed by public health agencies. To assist with understanding the climatology and trend of temperature inversions for a large public health department, innovative pragmatic criteria were developed and used to determine morning and evening surface-based temperature inversions from datasets derived from Pittsburgh National Weather Service (NWS) radiosonde measurements made from 1 January 1991 through 31 December 2020. During this 30-year period, the strength of the morning (7 a.m. EST; 12 UTC) inversions was 3.9 °C on average. The depth of the inversion layer measured an average height of 246 m above the ground. The inversions tended to dissipate by 10 a.m. EST. The frequency of occurrence of morning inversions averaged 47%. The mean strength of the evening (7 p.m. EST; 00 UTC) inversions was 1.1 °C with a mean depth of 101 m above the ground. The frequency of evening inversion occurrence averaged 20% during this period. The 30-year climatology revealed generally declining frequency of inversions in the Pittsburgh area. Morning surface-based inversion strengths usually declined while morning depths and break times were steady. Evening inversion strengths and depths increased overall during the 30-year period. Monthly means showed a morning-evening overlap of some months that record the most frequent substantial inversions during the fall time of the year, coinciding with the time when the worst air pollution events occur.

Inclusive green growth (IGG) offers an effective alternative to pursue sustainable development. The core of the IGG system lies in the coordination of inclusive, green, and growth subsystems. However, there is little quantitative assessment on IGG based on subsystem collaboration. This study proposes a holistic scheme of inclusive-green-growth nexus in Anhui province from 2009 to 2018 by using an integrated approach, namely, the entropy weight approach (EWA), coupling coordination degree model (CCDM), grey prediction model (GPM), and obstacle factor diagnostic model (OFDM). The results show that: (1) The proposed integrated approach could be viable to measure the synergistic interactions among internal IGG subsystems; (2) At the provincial level, a relatively high IGG performance but a low coupling coordination degree (CCD) of the IGG nexus are seen. Although the predicted value of CCD will show an upward trend, it will not be able to cross the start stage. The obstacle factors on the coordinated development of IGG can be divided into two stages: (3) At the prefectural level, the cities in which CCD is rising outnumber those it is falling. However, the CCD is also low, and the gap between cities is getting wider. The obstacles that affect the CCD of cities see a dynamic evolution trend from “inclusive obstacle type” to “inclusive and growth obstacle type” then to “green obstacle type” over the decade.

Copper oxide nanoparticles (CuO NPs) have received considerable attention for their toxic effects on crops and potential application in agriculture. In order to investigate the biological effects of CuO NPs on plants, we exposed cucumber (Cucumis sativus) to two sizes of CuO NPs (510 nm, μCuO and 43 nm, nCuO). Results indicated that with concentration increased, the available Cu content in soil increased significantly. The addition of CuO NPs increased Cu content and other nutrient element (e.g., K, P, Mn, and Zn) content in plants. However, diverse particle sizes had different effects. The nCuO treatment had larger translocation factor, higher nutrient element content in fruits, and lower oxidative damage than μCuO treatment. Moreover, nCuO of 100 mg/kg could stimulate cucumber growth, while μCuO had no obvious effects on growth. Conclusively, CuO NPs could be used as copper fertilizer to supply copper to cucumber. The nCuO had better effects on improving the bioavailability of Cu and nutritional value of fruits. These results can help develop strategies for safe disposal of CuO NPs as agricultural fertilizer.

Forested catchments surround most of the riverine systems in the Niger Delta area of Nigeria. Presently, there are no biomonitoring tools to assess the ecological health of forested rivers in the area. Hence, this study is aimed at developing a macroinvertebrate-based multimetric index for assessing the health forested riverine systems in the Niger Delta area of Nigeria. Physico-chemical variables and macroinvertebrates were sampled from 18 sites in 11 rivers. Physico-chemical variables were used to categorise river sites into three ecological classes, which include near natural sites (NNS), slightly disturbed sites (SDS) and moderately disturbed sites (MDS). Fifty-four(54) macroinvertebrate candidate metrics in four groups which include abundance, composition, richness and diversity were selected for the development of the Niger Delta forested rivers multimetric index (FoMIND). Of the 54 metrics selected, four were integrated into the FoMIND, and they include EPT/Chironomidae abundance, %ETOC, Diptera richness and evenness index. Correlation of integrated metrics with physico-chemical variables on redundancy analysis (RDA) ordination showed that EPT/Chironomidae abundance and evenness index positively associated with dissolved oxygen at NNS which confirmed the near-natural conditions of most of the forested riverine systems in the Niger Delta. Therefore, FoMIND is recommended as a rapid diagnostic tool for monitoring and assessing water quality conditions of rivers within forested catchments in Nigeria.

Although domestic wastewater originating from ships is discharged to the sea after being treated in the treatment system, it cannot meet the wastewater concentration standards determined by the authorities in terms of some pollutant concentrations. This problem is more important on cruise ships, which can carry much more people than other commercial ships. After the wastewater treated in the treatment system on the ship is discharged to the sea, it is subjected to a secondary natural treatment due to the turbulence that occurs on the ship’s trail. This phenomenon, called dilution, helps the pollutant concentrations in high concentrations to reach the wastewater standards determined by the authorities in a short time. The magnitude of this dilution is called the dilution factor. In this study, gross ton, deadweight ton, passenger number, freeboard, engine power, propeller number, and block coefficient data of a total of 1942 passenger ships, 941 of which were small and 1041 of which were large passenger ships, were used in artificial neural networks to determine which parameter was more effective in calculating the dilution factor. Engine power and gross ton value were determined as the most effective parameters for the dilution factor, and it was seen that by using these parameters alone in artificial neural networks, the dilution factor could be successfully predicted regardless of whether the ship was small or large. Finally, the effect of dilution was assessed in terms of sustainable development goals and life cycle perspective.

In recent years, the sustainable development of islands has attracted increasing attention from countries all over the world. An important prerequisite for promoting sustainable development is to understand the foundation and sustainable development potential of islands. Constructing index systems and models is an important means of evaluating the sustainability of islands. This study used factor analysis (FA) to construct an indicator system and set weights. Thirty-eight indicators were set from both natural and social directions to evaluate the sustainable development of seven typical islands in China. The FA removed the 10 indicators that were too relevant, and the 28 effective indicators were reduced into 9 main factors for evaluation. The results showed that the evaluation results are in line with the actual development of the island, which verifies the applicability of the model to different types of islands. The study also found that the changing trends of island social sustainability, tourism sustainability, ecological sustainability, resource sustainability, and economic sustainability are consistent. The value of fully balanced islands is higher than that of unbalanced or undeveloped islands. Among the seven islands, social islands have the highest total value, and ecological islands have the lowest total value.

Organic substrates are gaining popularity as a means of mitigating the negative effects of cadmium (Cd) stress on plant growth. The aim of the present study was to investigate the physio-biochemical attributes of Berberis integerrima bunge under Cd-contaminated soil. The pot experiment was carried out based on a completely randomized design (CRD) with six replicates. Cd stress was used as cadmium chloride (CdCl₂) at 10, 20, and 30 mg Cd kg⁻¹ dry soil. Biochar was applied at the doses of 125 g per pot, and vermicompost was used at the doses of 250 g per pot separately, and for their combination, they were used as 125 g per pot of BC + 250 g per pot of VC. The results showed higher Cd accumulation in both roots and leaves when the soil was polluted with Cd concentrations, but both BC and VC decreased the Cd accumulation in plant tissues. Although chlorophyll content and relative water content (RWC) decreased at 20 and 30 mg Cd kg⁻¹ soil, BC and VC, particularly their combination, increased these traits. The highest total phenolic content (TPC) was observed in plants exposed to 20 mg Cd kg⁻¹ soil and combined BC and VC. The total flavonoid content (TFC) was increased to 20 mg Cd kg⁻¹ soil and then decreased to 30 mg Cd kg⁻¹ soil. In addition, organic fertilizer promoted the plants’ high accumulation of TFC. The greater activities of antioxidant enzymes including superoxide dismutase (SOD) and phenylalanine ammonia-lyase (PAL) were observed at 30 mg Cd kg⁻¹ soil when organic substrates were added. The present study suggests the use of combined BC and VC lead to alleviate the adverse effects of Cd stress in B. integerrima.

The multi-dimensional pollutions in the earth zone due to the degradation of the environmental levels have been emerging as an urgent issue in the developing economies. The BRICS group of countries holds a unique position in the emerging economies, playing a leading role in reinforcing political power globally and domestically. This study examines the annual time series over the period of 1971–2017 for Brazil, India, China, and South Africa, and 1990–2017 for Russia, to explore the relationship between environmental quality and economic growth in correspondence with the Environmental Kuznets Curve (EKC) hypothesis as well as the Decoupling Index (DI). The presence of an EKC strongly supports any of the individualistic environmental determinants effected in the long run by Autoregressive Distributed Lag (ARDL), building linkage with the approach of the co-integration and the DI, which brings about economic growth as well as CO₂ emission and environmental degradation simultaneously. A short-run relationship and presence of the EKC hypothesis are observed in Brazil, Russia, and India with 92%, while China’s (55%) and South Africa’s (79%) have a slower speed of adjustment to long-run equilibrium. This study concludes that economic expansion and environmental degradation are interrelated in the long run. Environment degradation (CO₂ emission) can be eradicated by continuous economic growth, management of energy demands and energy crises, implementation of environmentally sustainable policies, application of green technologies for the use of natural resources, and controllability of urban population growth with immediate and effective actions.

Rational prediction of future CO₂ at the regional level is essential to the carbon emission reduction targets in China. The primary aim of this study is to examine the applicability of an up-to-date forecast algorithm, namely dynamic mode decomposition (DMD), in provincial CO₂ emission prediction. The testing results validate the accuracy and application value of the DMD short-run forecast, which may provide method reference for relevant policy formulation and research areas. Moreover, the 2020 provincial economic situation and CO₂ emissions in China are projected via DMD. On this basis, the unqualified provinces regarding CO₂ emission reduction are identified considering the relative standard and absolute standard, and the corresponding mitigation paths are proposed through decoupling analysis and shadow price calculation. The results indicate that the unqualified provinces include Heilongjiang, Gansu, Shanxi, Hebei, Liaoning, Jilin, Shaanxi, and Inner Mongolia. The open-emission-reduction mechanism should be adopted in the first five provinces; the conservative one should be applied in the other provinces. Graphical abstract

The decision of intensive care unit (ICU) admission in acute pesticide poisoning is often challenging, especially in developing countries with limited resources. This study was conducted to compare the efficacy of the Acute Physiology and Chronic Health Evaluation II (APACHE II), Modified Early Warning Score (MEWS), and Poisoning Severity Score (PSS) in predicting ICU admission and mortality of acute pesticide-poisoned patients. This prospective cohort study included all patients admitted to Tanta University Poison Control Center with acute pesticide poisoning from the start of March 2018 to the end of March 2019. Patient data, including demographic and toxicological data, clinical examination, laboratory investigation, and score values, were collected on admission. Out of 337 acute pesticide-poisoned patients, 30.5% were admitted to the ICU, including those poisoned with aluminum phosphide (ALP) (81.5%) and organophosphates (OP) (18.5%). Most non-survivors (86.6%) were ALP poisoning. The PSS had the best discriminatory power in predicting ICU admission and mortality, followed by APACHE II and MEWS. However, no significant difference in predicting ICU admission of OP-poisoned patients was detected between the scores. Additionally, no significant difference in mortality prediction of ALP-poisoned patients was found between the PSS and APACHE II. The PSS, APACHE II, and MEWS are good discriminators for outcome prediction of acute pesticide poisoning on admission. Although the PSS showed the best performance, MEWS was simpler, more feasible, and practicable in predicting ICU admission of OP-poisoned patients. Moreover, the APACHE II has better sensitivity for mortality prediction of ALP-poisoned patients.