Increasing Pollution Levels Research Articles

About Air Quality Assessment using Time Series Analysis Techniques on Air Particles Data

This study investigates the dynamics of air pollution caused by particulate matter (PM10 and PM2.5) in Omsk, Russia. Data collected from the Sensor.Community network between 2021 and 2023 were analyzed using time series analysis methods. The findings reveal a subtle weekly seasonality in the data, potentially attributed to fluctuations in industrial activity and traffic patterns throughout the week. Additionally, a concerning trend of deteriorating air quality was identified, characterized by increasing annual average concentrations of PM10 and PM2.5. The study employed various time series analysis techniques, including Empirical Cumulative Distribution Function (ECDF), Seasonal and Trend decomposition using Loess (STL), and autocorrelation function (ACF) analysis. These methods allowed for the visualization and examination of data distribution, trend identification, and seasonality detection. Furthermore, the Mann-Kendall test confirmed a statistically significant trend of increasing pollution levels over the three-year period. To enhance the accuracy of spatial analysis, data interpolation was performed using kriging with a Gaussian model. This method, compared to simple interpolation, provides a more precise assessment of the spatial distribution of pollutants. The study also implemented Long Short-Term Memory (LSTM) for forecasting hourly pollution values. LSTM exhibited promising results, demonstrating its potential for developing early warning systems, particularly when combined with meteorological data such as wind forecasts. The results highlight the need for effective measures to mitigate air pollution in Omsk and emphasize the importance of utilizing data from diverse sources, including official air quality monitoring stations, to ensure accuracy and reliability in air quality assessments.

Interplay of Foreign Direct Investment, Remittances, and Economic Growth: Insights from SAARC Countries

The current study focuses on finding and analyzing the nexus between remittances, foreign direct investment, and economic growth in SAARC countries. This study uses secondary balanced data of selected SAARC countries for the years 2000 to 2019. This study uses gross domestic product i.e., GDP, which serves as the proxy of economic growth, as the dependent variable. The core independent variables of this study include (FDI) foreign direct investment and remittances (RMTN). Additionally, gross capital formation (GCF) and carbon dioxide emissions (CO2) are the other two independent variables in this study. For the proxy for environmental degradation the study uses data of CO2 emissions. The data is collected from the world development indicators, WDI. The study focuses on how all the independent variables affect the growth of economy in selected SAARC countries which include Pakistan, India, Bangladesh, Sri Lanka, and Nepal. The study applies cross sectional dependence test, panel cointegration test, unit root tests, and PARDL (panel auto regressive distributed lag model), also called pooled mean group methodology, to obtain empirical estimates. The results of PARDL show that FDI, RMTN, and GCF are all positively and significantly related to GDP. This means that GDP or alternatively economic growth, is improving with rise in FDI, RMTN, and GCF in selected SAARC countries. The results show that CO2 emissions have a negative significant impact on GDP i.e., increase in pollution levels is harmful for the growth of the selected economies.

Can environmental taxes and green technological investment ease environmental pollution in China?

This study examines the complex relationship between environmental taxation, green technology, green investment, real GDP, and environmental pollution by employing the Nonlinear autoregressive distributive lag model for China. The findings indicate that environmental tax policies have significant short- and long-term effects, with positive shocks leading to pollution reductions. The positive and negative shocks to green investment increase pollution levels in the long run. Moreover, the real GDP displays pollution reductions in the short run. The results provide valuable insights into China's environmental and economic situation and offer policy recommendations. These findings were confirmed using the Wald test. The findings emphasize China's need for stronger environmental taxes, green technology adoption, evaluated green investments, and sustainable economic growth to achieve cleaner production and environmental sustainability.

중국 환경보호정책의 효과성 분석: 시(市) 단위 패널자료를 중심으로

This study explores the effect of China's environmental protection policies on environmental pollution. In order to investigate the actual effect of environmental policies implemented in 31 provincial capitals of China and explore the relationship between environmental pollution and economic growth, panel analysis was carried out. The Panel's analysis shows that environmental policies can reduce pollution levels and economic growth can increase pollution levels. These results suggest that China should strengthen its environmental protection policy in the future. For example, in the field of industry, the focus is on upgrading governance equipment and technology, and strengthening supervision and punishment. In terms of transportation, strict emission standards are established, public transportation and new energy tools are developed, and path planning is optimized. In the economic field, preferential policies should be adopted to promote green industries and eliminate industries with serious pollution. Urban planning should be rationalized and green areas should be increased. At the same time, raise awareness of environmental protection and encourage the public to participate in environmental protection activities.

Contribution of ethnicity, area level deprivation and air pollution to paediatric intensive care unit admissions in the United Kingdom 2008–2021

There is emerging evidence on the impact of social and environmental determinants of health on paediatric intensive care unit (PICU) admissions and outcomes. We analysed UK paediatric intensive care data to explore disparities in the incidence of admission according to a child's ethnicity and the degree of deprivation and pollution in the child's residential area. Data were extracted on children <16 years admitted to UK PICUs between 1st January 2008 and 31st December 2021 from the Paediatric Intensive Care Audit Network (PICANet) database. Ethnicity was categorised as White, Asian, Black, Mixed or Other. Deprivation was quantified using the 'children in low-income families' measure and outdoor air pollution was characterised using mean annual PM2.5 level at local authority level, both divided into population-weighted quintiles. UK population estimates were used to calculate crude incidence of PICU admission. Incidence rate ratios were calculated using Poisson regression models. There were 245,099 admissions, of which 60.7% were unplanned. After adjusting for age and sex, Asian and Black children had higher relative incidence of unplanned PICU admission compared to White (IRR 1.29 [95% CI: 1.25-1.33] and 1.50 [95% CI: 1.44-1.56] respectively), but there was no evidence of increased incidence of planned admission. Children living in the most deprived quintile had 1.50 times the incidence of admission in the least deprived quintile (95% CI: 1.46-1.54). There were higher crude admission levels of children living in the most polluted quintile compared to the least (157.8 vs 113.6 admissions per 100,000 child years), but after adjustment for ethnicity, deprivation, age and sex there was no association between pollution and PICU admission (IRR 1.00 [95% CI: 1.00-1.00] per 1μg/m3 increase). Ethnicity and deprivation impact the incidence of PICU admission. When restricting to unplanned respiratory admissions and ventilated patients only, increasing pollution level was associated with increased incidence of PICU admission. It is essential to act to reduce these observed disparities, further work is needed to understand mechanisms behind these findings and how they relate to outcomes. There was no direct funding for this project. HM was funded by an NIHR Academic Clinical Fellowship (ACF-2022-18-017).

Metal(loid) uptake and physiological response of Coix lacryma-jobi L. to soil potentially toxic elements in a polluted metal-mining area

Coix lacryma-jobi L. is a traditional medicinal plant in east Asia and is an important crop in Guizhou province, southwest China, where there are elevated levels of soil mercury and arsenic (As). Exposure to multiple potentially toxic elements (PTEs) may affect plant accumulation of metal(loid)s and food safety in regions with high geological metal concentrations. Field experiments were conducted to study the effects of PTEs on metal(loid) accumulation and physiological response of C. lacryma in different plant parts at three pollution levels. Total root length, number of root tips, number of branches, and number of root crosses increased with increasing pollution level, with increases in highly polluted areas of 44.2, 57.0, 79.6, and 97.2%, respectively, compared to lightly polluted areas. Under multi-element stress the activity of C. lacryma antioxidant oxidase showed an increase at low and medium PTE concentrations and inhibition at high concentrations. The As contents were all below the maximum limit of cereal food contaminants in China (GB 2762-2022, As < 0.5 mg kg−1). The stems had high Tl bioconcentration factors but the translocation factors from stem to grain were very low, indicating that the stems may be a key plant part restricting Tl transport to the grains. C. lacryma increased root retention and reduced the transport effect, thus reducing metal accumulation in the grains. C. lacryma adapted to PTE stress through root remodeling and enhanced antioxidant enzyme activities.

Corporate Carbon Footprint: Evaluating Environmental Risks and Incentives in the Era of Climate Finance

To fight climate change and reduce their increasing pollution levels, developing nations must have access to worldwide public climate funds. In the view of developing nations, meeting their commitments to reduce emissions under the Paris Agreement depends on receiving funding for climate-related initiatives. According to earlier studies, financing developing nations may help them meet their climate targets. The most recent data on climate money and NDCs are used in this research to examine the subject from a practical standpoint. Two research approaches were used in this study to investigate the impact of climate funding on the objectives of recipient nations. Data indicated that it was excellent regardless of the size of the effect. Developing nations’ attempts to reduce emissions were more affected than those of the least developed nations, especially those with economies centred on tiny islands. This paper outlines how to solve the issue, but it is just the beginning. By informing their handling of climate money, investors can gain poorer nations’ confidence, according to this research.

Assessment of Soil Contamination with Heavy Metals and Arsenic in the City of Komsomolsk-on-Amure

Research and assessment of soil contamination with heavy metals and arsenic in Komsomolsk-on-Amur have been carried out. High levels of soil contamination with heavy metals have been found — toxicants of hazard classes 1 and 2, among which the largest participation of zinc, lead, copper and cadmium has been recorded. It has been noted that the high content of arsenic in soils may be due to both technogenic influence and natural features of the area under study. A comparison has been made of the contents of heavy metals and arsenic in the soils of the city of Komsomolsk-on-Amur with the local natural background and technogenic background concentrations of other industrial cities in Russia and China. Two main technogenic geochemical anomalies (zones of increased pollution levels) associated with the main industrial hubs of the city have been identified.

Estimating thresholds of natural vegetation for the protection of water quality in South African catchments

Many of South Africa's current water quality problems have been attributed to diffuse pollution derived from poorly regulated land use/land cover (LULC) transformations. To mitigate these impacts, the preservation of an adequate amount of natural vegetation within catchment areas is an important management strategy. However, it is not clear how much natural vegetation cover is required to provide adequate levels of protection, nor at which scale(s) this strategy would be most effective. To investigate the possibility of estimating minimum thresholds of natural vegetation required to protect water resources, regression analysis was used to model relationships between water quality (measured using Nemerow's Pollution Index) and metrics of natural vegetation at multiple scales across a sample of sub-catchments located along the western, southern, and south-eastern coast of South Africa. With conspicuous outliers removed, the models were able to explain up to 82 % of the variability in the relationship between land use and water quality. Moreover, a statistically significant, nonlinear, and inverse relationship was found between proportions of natural vegetation cover and pollution levels. This relationship was strongest when measured (1) across the whole catchment and (2) within a 200 m riparian buffer zone. The models further indicated that approximately 80 to 90 % natural vegetation cover was necessary at these scales to maintain water quality at ecologically acceptable levels. Additional nonlinear thresholds estimated using breakpoint analysis suggested that if proportions of natural vegetation fall below 45 % (across the whole catchment) and 60 % (within a 200 m riparian buffer zone) a dramatic increase in pollution levels can be expected. The estimated thresholds are recommended as guidelines that can be used to inform integrated land and water resources management strategies aimed at protecting water quality in the study area. Likewise, the methods described are recommended for the estimation of similar thresholds in other regions.

Impact of injection pressure on the performance, emissions, and combustion of a common rail direct injection engine fueled with quaternary blends

The scarcity and rising costs of fossil fuels, coupled with increasing pollution levels, have prompted the exploration of innovative biofuel blends. While binary and ternary fuels have been studied extensively in proportions of 5–20%, replacing up to 30–40% of fossil fuel dependency without significant engine modifications remains a challenge. One potential solution is to investigate an optimal quaternary blend (QB) through experimental methods. This study investigates the impact of increased injection pressure (IOP) and quaternary fuel blends on a common rail direct injection (CRDI) engine's performance and emissions. Different blends, including diesel fuel, vegetable oil, mahua methyl ester and normal-butanol, were tested to replace 30–40% of diesel and enhance combustion, reduce exhaust emissions and improve overall performance. Experiments used a 1-cylinder CRDI engine at high IOPs (400, 500, 600 and 700 bar). Results at 600 bar IOP showed that the optimal blend, QB3–QB4, increased brake thermal efficiency (BTE) by 9% and reduced brake-specific fuel consumption (BSFC) by approximately 11% compared to other blends. Emissions at 600 bar included a 16% reduction in hydrocarbons (HC) and a 24% decrease in carbon monoxide (CO) at full load. However, nitrogen oxide (NOx) emissions slightly increased with higher IOP. Significantly employing the QB3–QB4 blend at 600 bar improved control over HC, CO and smoke emissions. Overall, performance was enhanced and comparable to conventional diesel fuel, with only a minor increase in NOx emissions.

A Systematic Review for Indoor and Outdoor Air Pollution Monitoring Systems Based on Internet of Things

Global population growth and increasing pollution levels are directly related. The effect does not just apply to outdoor spaces. Likewise, the low indoor air quality is also having a negative impact on the health of the building residents. According to the World Health Organization, indoor air pollution is a leading cause of 1.6 million premature deaths annually. Tackling this public health issue, due to the direct relationship between air pollution levels and mortality and morbidity rates as well as overall comfort, is mandatory. Many companies have begun to build inexpensive sensors for use in Internet of Things (IoT)-based applications to pollution monitoring. The research highlights design aspects for sustainable monitoring systems including sensor types, the selected parameters, range of sensors used, cost, microcontrollers, connectivity, communication technologies, and environments. The main contribution of this systematic paper is the synthesis of existing research, knowledge gaps, associated challenges, and future recommendations. Firstly, the IEEE database had the highest contribution to this research (48.51%). The results showed that 87.1%, 66.3%, and 36.8% of studies focused on harmful gas monitoring, thermal comfort parameters, and particulate matter levels pollution, respectively. The most studied harmful gases were CO2, CO, NO2, O3, SO2, SnO2, and volatile organic compounds. The cost of the sensors was suitable for people with limited incomes and mostly under USD 5, rising to USD 30 for specific types. Additionally, 40.35% of systems were based on ESP series (ESP8266 and ESP32) microcontrollers, with ESP8266 being preferred in 34 studies. Likewise, IoT cloud and web services were the preferred interfaces (53.28%), while the most frequent communication technology was Wi-Fi (67.37%). Indoor environments (39.60%) were the most studied ones, while the share for outdoor environments reached 20.79% of studies. This is an indication that pollution in closed environments has a direct impact on living quality. As a general conclusion, IoT-based applications may be considered as reliable and cheap alternatives for indoor and outdoor pollution monitoring.

Role of resources rent, research and development, and information and communication technologies on CO2 emissions in BRICS economies

The continuous increase in pollution levels has become a severe threat to the globe, and practitioners are trying their best to deal with environmental challenges to attain for attaining the targets of sustainable development goals (SDGs). Under the environmental aspects, the world has introduced numerous solutions to become clean and green while the problem remains intact. Therefore, this study is an attempt that investigate the impact of natural resources rent, research & development expenditures (R&D), information and communication technologies, and renewable energy consumption on carbon emissions under the framework of the STIRPAT model for BRICS economies during 1998–2021 by using CS-ARDL method. The empirical outcome shows that economic growth (EG), urbanization, and natural resources increase environmental pollution. On the other hand, R&D expenditures, renewable energy and digitalization support the sustainability theme. Furthermore, this study uses the Augmented Mean Group, Common Correlated Effect Mean Group and Panel Quantile Regression to validate the prior outcomes. Finally, the inverted connection between natural resource rents and emissions is validated in BRICS economies. Based on the empirical outcomes, this study suggests policy implications for attaining the targets of SDG-13 (climates change), SDG-07 (affordable and clean energy), SDG-08 (decent economic growth), SDG-09 (innovations, research and development), SDG-11 (sustainable cities and societies), and SDG-12 (responsible consumption).

Recent progress in defect-engineered metal oxides for photocatalytic environmental remediation.

Rapid industrial advancement over the last few decades has led to an alarming increase in pollution levels in the ecosystem. Among the primary pollutants, harmful organic dyes and pharmaceutical drugs are directly released by industries into the water bodies which serves as a major cause of environmental deterioration. This warns of a severe need to find some sustainable strategies to overcome these increasing levels of water pollution and eliminate the pollutants before being exposed to the environment. Photocatalysis is a well-established strategy in the field of pollutant degradation and various metal oxides have been proven to exhibit excellent physicochemical properties which makes them a potential candidate for environmental remediation. Further, with the aim of rapid industrialization of photocatalytic pollutant degradation technology, constant efforts have been made to increase the photocatalytic activity of various metal oxides. One such strategy is the introduction of defects into the lattice of the parent catalyst through doping or vacancy which plays a major role in enhancing the catalytic activity and achieving excellent degradation rates. This review provides a comprehensive analysis of defects and their role in altering the photocatalytic activity of the material. Various defect-rich metal oxides like binary oxides, perovskite oxides, and spinel oxides have been summarized for their application in pollutant degradation. Finally, a summary of existing research, followed by the existing challenges along with the potential countermeasures has been provided to pave a path for the future studies and industrialization of this promising field.

Influence of meteorological variables and air pollutants on fog/smog formation in seven major cities of Indo-Gangetic Plain.

The Indo-Gangetic Plains (IGP) of the Indian subcontinent during winters experience widespread fog episodes. The low visibility is not only attributed to meteorological conditions but also to the increased pollution levels in the region. The study was carried out for Tier 1 and Tier II cities of the IGP of India, including Kolkata, Amritsar, Patiala, Hisar, Delhi, Patna, and Lucknow. This work analyzes data from 1990 to 2023 (33years) employing the Mann-Kendall-Theil-Sen slope to determine the trends in fog occurrences and the relation between fog and meteorological parameters using multiple linear regressions. Furthermore, identifying the most relevant fog (visibility)-impacting factors from a set of both meteorological factors and air pollutants using step-wise regression. All cities indicated trend in the number of foggy days except for Kolkata. The multiple regression analysis reveals relatively low associations between fog occurrences and meteorological factors (30 to 59%), although the association was stronger when air pollution levels were considered (60 to 91%). Relative humidity, PM2.5, and PM10 have the most influence on fog formation. The study provides comprehensive insights into fog trends by incorporating meteorological data and air pollution analysis. The findings highlight the significance of acknowledging meteorological and pollution factors to understand and mitigate the impacts of reduced visibility. Hence, this information can guide policymakers, urban planners, and environmental management agencies in developing effective strategies to manage fog-related risks and improve air quality.

Pollution Characteristics, Source, and Health Risk Assessment of Metal Elements in PM2.5 Between Winter and Spring in Zhengzhou

A total of 18 metal elements in ambient PM2.5 in Zhengzhou were continuously determined using an online heavy metal observation instrument in January and April, 2021, and the changes in element concentrations were analyzed. Metal elements were traced via enrichment factors, positive matrix factorization (PMF), and a characteristic radar chart. The US EPA health risk assessment model was used to assess the health risks of heavy metals, and the backward trajectory method and the concentration-weighted trajectory (CWT) method were used to evaluate the potential source regions of health risks. The results showed that the element concentrations were higher in spring, and the sum of Fe, Ca, Si, and Al concentrations accounted for 89.8% and 87.5% of the total element concentrations in winter and spring, respectively. Cd was enriched significantly, which was related to human activities. The concentrations of Pb, Se, Zn, Ni, Sb, and K in winter and Cr, Ni, Fe, Mn, V, Ba, Ca, K, Si, and Al in spring increased with the increasing pollution level. The results of PMF and the characteristic radar chart showed that the main sources of metal elements in winter and spring were industry, crust, motor vehicles, and mixed combustion, with industry and mixed combustion pollution occurring more often in winter and crust pollution occurring more often in spring. Significant non-carcinogenic risks existed in both winter and spring with more severe health risks in winter, and Mn caused significant non-carcinogenic risks. The health risks in winter were mainly influenced by Zhengzhou and surrounding cities and long-distance transport in the northwest, and the health risks in spring were mainly influenced by Zhengzhou and surrounding cities.

Urban trees' potential for regulatory services in the urban environment: an exploration of carbon sequestration.

Urbanisation has emerged as a formidable challenge for urban policymakers, reaching unparalleled heights and unsettling the ecological equilibrium of the cities. Urban areas now grapple with many issues encompassing climate change, resource depletion, population surges and increased pollution levels. Many planned cities have planted trees and other vegetation within the urban sectors to enhance air quality, mitigate climate effects and provide valuable ecosystem services. This study assessed tree species diversity and their potential for carbon sequestration in Panjab University Campus, Chandigarh. We established 188 plots, each comprising randomly selected quadrats measuring 10 m × 10 m, encompassing areas with varying levels of vegetation, ranging from low to moderate and high density. We used four different allometric equations to estimate tree biomass and carbon stock. Our findings revealed that 92 tree species belong to 72 genera and 35 families, with a total tree density of 975 ha-1. The total CO2 sequestration in form of carbon stock was 18,769.46 Mg C ha-1, with Manilkara hexandra (1239.20 Mg C ha-1), Ficus benghalensis (1072.24 Mg C ha-1), Kigelia pinnata (989.89 Mg C ha-1) and Lagerstroemia floribunda (716.88 Mg C ha-1) being the top contributors. Specifically, the equation of Chave et al. (2005) without tree height yielded the highest biomass and carbon stock estimates than other equations. The present study underscores the vital role of trees on the campus as potent carbon reservoirs meet to maintain an aesthetic sense for biotic components and alleviate rising levels of CO2 in the atmospheric environment. By emphasising the role of urban trees as potent carbon reservoirs, the study underscores the importance of integrating green infrastructure into urban planning strategies. Furthermore, it offers valuable guidance for urban planners. It suggests that strategic tree planting and maintenance can enhance green spaces, regulate temperatures and ultimately support regional and global climate change mitigation goals. Incorporating these findings into urban planning processes can aid policymakers in developing resilient, ecologically sustainable cities worldwide.

Trace compounds in the urban water cycle in the Freiberg region, Germany

Trace compounds in the urban water cycle in the Freiberg region, Germany

Machine Learning for Water Quality Index Forecasting

This study aims to forecast water quality in the Tumkur district, Karnataka state, India, to increase pollution levels. Various machine learning techniques, including support vector machines, regression trees, linear regression, and neural networks, are employed. The Water Quality Index (WQI) is determined using parameters such as total hardness, pH, alkalinity, turbidity, chloride, dissolved solids, and conductivity. The dataset is split into training and testing sets (80:20) to assess model performance. Support Vector Machines and Linear Regression outperform other models, achieving R2 values of 0.96 and 0.99 for training and testing, respectively. This research underscores the importance of advanced machine learning techniques for accurate water quality prediction, crucial for effective pollution reduction strategies in the region.

ARTISTIC INTERPRETATIONS OF WATER ISSUES IN THE CONTEXT OF URBANISM : THE YAMUNA RIVER IN DELHI

The present paper explores the reflection of water issues within urban spaces in contemporary art practices, specifically focusing on the Yamuna river in Delhi. The Yamuna holds historical and religious significance and plays a crucial role in meeting the water needs of Delhi's 31.2 million population. However, the rapid urbanization of Delhi has led to detrimental consequences for the river, including increased pollution levels, encroachment on river banks, water scarcity, reduced water flow and detachment from its surroundings. The paper examines contemporary artistic interventions that address these ecological concerns through new media, site-specific art projects and public art. The aim of this paper is to identify those artists and to discuss the various artistic modalities in which these ecological concerns are addressed in the form of aesthetical structures and to discover how art is functionalized as a social and collective agent to create awareness among the public. It also aims to explore how artists are incorporating the ecological aspect of Urbanity in their works. the methodology involves interviews with artists, where their creative insights are explored to discern the multifaceted relationship between the river's ecological challenges and urban development. The study employs an interdisciplinary approach that sheds light on the complex interplay between environmental concerns and artistic expression. the paper concludes how art functions as a social and collective agent, raising awareness among the public about the ecological urgency caused by rampant urbanization.

Starch‐PHA Blend‐Based Biopolymers with Potential Food Applications

AbstractThe growing population and societal consumption patterns associated with unsustainable production routes have increased pollution levels. Because of the environmental pollution caused by petroleum‐derived plastics, it has become critical to develop an alternative biopolymer‐based biodegradable plastic for long‐term sustainability. Biopolymers are eco‐friendly due to their nontoxicity, biodegradability, stability, biocompatibility, rheological, and thermal properties. However, these polymers' production methods have hindered their progress toward a bright future of sustainable production and growth. Therefore, improved formulations and tactics are needed to develop new approaches. A range of methods for application in industry can transform Polyhydroxyalkanoates (PHAs). Due to its affordability and simplicity of use, blending is regarded as one of the most successful methods. It has various uses, from absorbable sutures to biomedical implants and ecologically friendly resins, and useful applications in food packaging. Starch is a highly economical raw material and can be processed as an affordable substrate for innovative biodegradable polymers. In contrast to lowering water vapor and oxygen permeability, the addition of cross‐linking agents to starch/PHA blends increases thermal stability, elasticity, and compatibility between the starch and PHA molecules in the films. This review focuses on the synthesis, physicochemical properties, and application of PHA‐starch blends in the food industry.