Household chemicals and their impact

Khalil, Memoona; Iqbal, Muhammad; Turan, Veysel; Tauqeer, Hafiz Muhammad; Farhad, Muniba; Ahmed, Awadelkarim Hamid; Yasin, Samia

doi:10.1016/b978-0-323-90555-8.00022-2

Cited by 47 publications

(3 citation statements)

References 162 publications

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“…While the stability of these factors is relatively high in the short term, variations in water availability, atmospheric, and thermal conditions resulting from natural environmental influences can exert a profound impact on the long‐term spatial configuration of land use patterns (Guo, Gao, et al, 2021). In relation to socio‐economic factors, HQ is influenced by urban expansion, population growth, and the implementation of ecological restoration projects, as these factors induce modifications in the structure, processes, and functions of ecosystems through changes in land use and land cover (LULC) (Abbas et al, 2022; Ahmad et al, 2022; Khalil et al, 2022; Wang, Ma, et al, 2022). The identification of key drivers that influence HQ and their interrelationships is crucial for comprehending the spatial and temporal variation in HQ.…”

Section: Introductionmentioning

confidence: 99%

Research on habitat quality in arid urban agglomerations: Influencing mechanisms and multi‐scenario simulations

Han,

Zheng,

Han

et al. 2024

Land Degrad Dev

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In the context of climate change and rapid urbanization, there have been unparalleled changes in land use and land cover (LULC), resulting in substantial impacts on the surrounding habitat quality (HQ), particularly in ecologically vulnerable arid regions. However, previous studies on the influencing mechanisms of HQ in arid urban agglomerations and future multi‐scenario simulations remain limited. To fill this knowledge gap, this study aimed to reveal the influencing mechanisms in HQ changes and to develop a multi‐scenario HQ assessment framework within arid urban agglomerations. We assessed the spatiotemporal variations in HQ using the InVEST model and three periods of LULC data for the urban agglomeration on the northern slope of the Tianshan Mountains (UANSTM), and the partial least squares structural equation model was introduced to explore the interactions between natural and non‐natural factors and their impacts on HQ. Additionally, we coupled multi‐objective programming and PLUS models to predict the LULC under different optimization scenarios (natural development scenario (NDS), ecological protection scenario (EPS), ecological–economic coordinated scenario, and economic development scenario) for the UANSTM in 2030, and to assess HQ. Results show that (1) the HQ index of the UANSTM was 0.507, 0.520, and 0.495 in 2000, 2010, and 2020 respectively, with a spatial distribution pattern of high values in the west, low values in the east, and high in the central and low in the north and south; (2) geomorphic, climatic, and LULC factors have direct positive effects on HQ, while socio‐economic factors have a direct negative effect on HQ. In addition, geomorphic, socio‐economic, and climatic factors also influence HQ through potential indirect paths. Climatic and LULC factors enhance the positive effect of geomorphic on HQ while counteracting the direct negative effect of socio‐economic factors on HQ. Climatic factors have the largest negative effect on HQ through their influence on LULC; (3) according to the four scenarios in 2030, the highest HQ index (increased by 0.13%) was found under the EPS, which also aligns more closely with SDGs. Conversely, NDS showed the lowest HQ index (declined by 2.59%). The research results could provide a scientific basis for promoting sustainable land management and ecological conservation for the UANSTM.

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Section: Introductionmentioning

confidence: 99%

Research on habitat quality in arid urban agglomerations: Influencing mechanisms and multi‐scenario simulations

Han,

Zheng,

Han

et al. 2024

Land Degrad Dev

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“…Potential harm to wildlife and human health is caused by bioaccumulation and potential biomagnification within the food chain [15,16]. Additional pollution sources, such as household chemicals [17,18] and microcontaminants like pesticides, biocides, medicines, and heavy metals [18], have been identified as significant contributors to environmental degradation and adverse health effects.…”

Section: Introductionmentioning

confidence: 99%

Towards a Sustainable Circular Economy: Algae‐Based Bioplastics and the Role of Internet‐of‐Things and Machine Learning

Bin Abu Sofian,

Lim,

Manickam

et al. 2023

ChemBioEng Reviews

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The growing potential of sustainable materials such as polyhydroxyalkanoates (PHAs), polylactic acid (PLA), alginate, carrageenan, and ulvan for bioplastics production presents an opportunity to promote a sustainable circular economy. This review investigates their properties, applications, and challenges. Bioplastics derived from algae offer an environmentally friendly alternative to petroleum‐based plastics, a shift of paramount importance to society due to the escalating environmental concerns associated with traditional plastics. The role of the internet‐of‐things (IoT) and machine learning in refining these bioplastics' production and development processes is emphasized. IoT monitors cultivation conditions, data collection, and process control for more sustainable production. Machine learning can enhance algae cultivation, increasing the supply of raw materials for algal bioplastics and improving their efficiency and output. The study results indicate the promise of algae‐based bioplastics, IoT, and machine learning in fostering a more environmentally sustainable future. By harnessing these advanced technologies, optimization of bioplastic production is possible, potentially revolutionizing the materials industry and addressing existing challenges toward achieving a sustainable circular economy.

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“…Even if small amounts of pesticide residues remain in the food supply, they constitute a potential risk to human health because of their subacute and chronic toxicity (Anaduaka et al 2023). Since some pesticides are carcinogenic and others can cause dysfunctions in the nervous and reproductive systems, they can be extremely harmful to human health, even at low concentrations (Khalil et al 2022).…”

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confidence: 99%

Pesticide residues in different honey types and public health risk assessment

Stevanović,

Idbeaa,

Bošković

et al. 2024

Acta Vet. Brno

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Honeybees and humans are endangered by pesticides in daily agricultural production. The aim of this research was to investigate pesticide residues in different honey types and to assess the risk to public health. A total of 88 honey samples originating from pine, multifloral, sunflower, acacia, linden, and canola were collected and analysed by a QuEChERS method. The hazard quotient (HQ) was used to evaluate the risk of detected pesticide residues. Analysis of pine honey did not detect any residue of investigated pesticides. The most frequently detected pesticides in the honey samples were chlorpyrifos ranging between 15.1 µg/kg (linden honey) to 22.3 µg/kg (multifloral honey), clothianidin ranging between 12.0 µg/kg (acacia honey) to 22.0 µg/kg (canola honey), dimethoate ranging between 8.9 µg/kg (multifloral honey) to 18.9 µg/kg (canola honey), and thiamethoxam ranging between 4.2 µg/kg (linden honey) to 15.6 µg/kg (canola honey), respectively. The lowest estimated daily intake (EDI) of 128 × 10-3 μg/kg of body weight per day was found in acacia honey, and the highest EDI of 265 × 10-3 μg/kg of body weight per day was found in canola honey. Similar values of EDI were determined for multifloral, sunflower, and linden honey (186 × 10-3, 187 × 10-3, and 183 × 10-3), respectively. The HQ value for pine honey was 0 indicating that this honey is the safest for consumption, however, the other types of honey investigated in this study posed no risk to humans after potential consumption.

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Household chemicals and their impact

Cited by 47 publications

References 162 publications

Research on habitat quality in arid urban agglomerations: Influencing mechanisms and multi‐scenario simulations

Research on habitat quality in arid urban agglomerations: Influencing mechanisms and multi‐scenario simulations

Towards a Sustainable Circular Economy: Algae‐Based Bioplastics and the Role of Internet‐of‐Things and Machine Learning

Pesticide residues in different honey types and public health risk assessment

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