Environmental Variables Research Articles

Attentiveness in urban spaces: The rhythm of the street

Driver vigilance research focuses on the safety impacts of maintaining sustained attention in underload conditions when automatic responses are adequate to the task and responses are sparse. However, urban environments have frequent risks that appear chaotically and require high levels of engagement—a very different vigilance task. Although drivers perceive a wide range of task demands as manageable, their movement patterns hint at more active engagement as they approach overload. This condition we are calling “Attentiveness,” to distinguish it from the classic exploration of vigilance in underload conditions. To understand how the built environment impacts driving automaticity and vigilance, data from the SHRP2 Naturalistic Driving Study (NDS) was used to evaluate driving patterns within urban multimodal street sections. Data for acceleration, jerk, lane position, and speed were tabulated for ten, 5-second epochs at 200 multimodal locations and compared to the behavior for the entire length of the drive. The built environment variables that demonstrated a strong correlation and impact size for a change in driver behavior included doorway density, block length, Walkscore, corridor aspect ratio (height over width), and terminated vistas. The commonality for these variables is that each one impacts the rate that novel stimuli arrive as drivers progress sequentially along their path. Acceleration, jerk, and lane position appear to be related to the rhythm of interruptions that occur along the length of the roadway.

The Influence of Work Discipline and Work Environment on Employee Work Productivity at the Mona Plaza Hotel Pekanbaru

This research aims to analyze the influence of work dicipline and employee work environment at the Mona Plaza Hotel Pekanbaru. The research method used is quantitative. The sample for this research was 32 employees with data collection techniques using questionnaires. The data obtained was analyzed through linear regression analysis with the SPSS program. The research results show; (1) There is a significant influence of work discipline on employee work productivity at the Mona Plaza Hotel Pekanbaru. The obtained tcount value > ttable or 8.361 > 1,697 with a significance of 0,000 < 0,05. The results of simple linear regression analysis show that if work discipline increases by 0,403, it will increase the work productivity of Mona Plaza Hotel Pekanbaru employees by 7,211 constantly. (2) There is a significant influence of the work environment on employee work productivity at Mona Plaza Hotel Pekanbaru. The work environment variable has a calculated tcount value > ttable or 6,873 > 1,697 with a significance of 0,000 < 0,05. The results of simple linear regression analysis show that if the work environment increases by 0,757, it will increase employee productivity at Mona Plaza Hotel Pekanbaru by 5,085 constantly. (3) There is an influence of work discipline and work environment on employee work productivity at the Mona Plaza Hotel Pekanbaru. From the results of the F test, it is known that the value of Fcount > Ftable is 42,988 > 3,32 with a significance value of 0,000 < 0,05.

Ecology and abundance of a relict population of the bush cricket Saga pedo in the Northern Apennines, Italy.

The expansion of forest cover and intensification of agriculture represent the main threats to the bush cricket Saga pedo, currently listed as Vulnerable globally by the IUCN and included in Annex IV of the European Union Habitats Directive. Gathering information on its ecology and population size is challenging due to its low abundance and localized distribution. Additionally, the elusive and cryptic behavior of this species reduces the likelihood of its detection, potentially resulting in population underestimations. Thus, in this study, we aimed to (1) estimate S. pedo population size in relation to environmental variables and prey availability and (2) predict abundance of S. pedo in our study area for future monitoring in nearby territories. We found that the population of S. pedo in our study area consists of 197 (±115) individuals with a detection probability of 21.01% (±11.09). Detection probability of S. pedo further decreases on windy days. Moreover, we found that the investigated population of S. pedo occupies suboptimal areas, as highlighted not only by the predicted abundances but also by the association between S. pedo and other subfamilies of orthoptera that are ecologically very distant from our target species and mostly linked to mesophilic biotopes. Most of the individuals we observed are concentrated in small clearings completely within wooded matrices and therefore isolated from each other. Based on our results, it is possible that forest expansion toward open meadows represents the main threat to this population, transforming the clearings and xeric meadows (to which S. pedo is linked) into small and fragmented patches that are suboptimal and insufficient to host viable populations.

Work engagement and sense of coherence as predictors of psychological distress during the first phase of the COVID-19 pandemic in Chile

ObjectivesThe aim of this study was to analyse the relationship between sense of coherence, work engagement, and work environment variables as predictors of the level of psychological distress during the first phase of the COVID-19 pandemic in Chile. MethodsCross-sectional descriptive study collected between April 22 and December 16, 2020, using non-probabilistic snowball sampling. The study variables and instruments were socio-demographic variables, work engagement (UWES-9 scale), sense of coherence (Antonovsky SOC-13 scale), and psychological distress (GHQ-12 scale). Multivariate analysis and binary logistic regression were performed including the scores of the three questionnaires and other variables such as effectiveness, safety, stress, health perception, and sex. Finally, the CHAID technique was applied to create a segmentation tree. Results72.7 % of participants had high levels of psychological distress, more predominantly among women, with work stress and low sense of coherence acting as the most influential mediators in generating psychological distress, and even more so when both were combined. Low work engagement and the availability of safe and effective means to prevent infection were predictors of psychological distress among workers. ConclusionDuring the first phase of the COVID-19 pandemic, factors that contributed to psychological distress in the Chilean population were identified. These included a fair or poor perception of health, being a woman, work-related stress, availability of safety measures, low level of work engagement, and low level of sense of coherence. Identifying these factors may help prevent similar effects in future phases of the current pandemic or in future pandemics.

A potential role of heat-moisture couplings in the range expansion of Striga asiatica.

Parasitic weeds in the genera Orobanche, Phelipanche (broomrapes) and Striga (witchweeds) have a devastating impact on food security across much of Africa, Asia and the Mediterranean Basin. Yet, how climatic factors might affect the range expansion of these weeds in the context of global environmental change remains unexplored. We examined satellite-based environmental variables such as surface temperature, root zone soil moisture, and elevation, in relation to parasitic weed distribution and environmental conditions over time, in combination with observational data from the Global Biodiversity Information Facility (GBIF). Our analysis reveals contrasting environmental and altitude preferences in the genera Striga and Orobanche. Asiatic witchweed (Striga asiatica), which infests corn, rice, sorghum, and sugar cane crops, appears to be expanding its range in high elevation habitats. It also shows a significant association with heat-moisture coupling events, the frequency of which is rising in such environments. These results point to geographical shifts in distribution and abundance in parasitic weeds due to climate change.

Environmental Factors Associated with Risk of Crohn’s Disease Development in the CCC-GEM Project

To date, it is unclear how environmental factors influence Crohn's Disease (CD) risk and how they interact with biological processes. This study investigates the association between environmental exposures and CD risk and evaluates their association with pre-disease biomarkers. We studied 4,289 healthy first-degree relatives (FDRs) of CD patients from the Crohn's and Colitis Canada - Genetic, Environmental, Microbial (CCC-GEM) project. Regression models identified environmental factors associated with future CD onset and their association with pre-disease biological factors, including altered intestinal permeability measured by urinary fractional excretion of lactulose to mannitol ratio(LMR); gut inflammation via fecal calprotectin(FCP) levels; and fecal microbiome composition through 16S rRNA sequencing. Over a 5.62-year median follow-up, 86 FDRs developed CD. Living with a dog between ages 5-15 (HR=0.62; 95% CI=0.40-0.96; P = .034), and living with a large family size in the first year of life (HR=0.43; 95% CI=0.21-0.85; P = .016) were associated with decreased CD risk; whereas having a bird at the time of recruitment (HR=2.78; CI=1.36-5.68; P = .005) was associated with an increased CD risk. Furthermore, living with a dog was associated with reduced LMR, altered relative abundance of multiple bacterial genera, and increased Chao1 diversity; while bird owners had higher FCP levels. Large family during participants' first year of life was associated with altered microbiota composition without affecting FCP or LMR. This study identifies environmental variables associated with CD risk. These variables were also associated with altered barrier function, subclinical inflammation, and gut microbiome composition shifts, suggesting potential roles in CD pathogenesis.

Response of Villorita cyprinoides and Magallana bilineata (Bivalvia) to Environmental Variables in Munroe Island, Kerala, India

Response of Villorita cyprinoides and Magallana bilineata (Bivalvia) to Environmental Variables in Munroe Island, Kerala, India

A polygenetic risk score combined with environmental factors better predict susceptibility to hepatocellular carcinoma in Chinese population.

This study aimed to investigate environmental factors and genetic variant loci associated with hepatocellular carcinoma (HCC) in Chinese population and construct a weighted genetic risk score (wGRS) and polygenic risk score (PRS). A case-control study was applied to confirm the single nucleotide polymorphisms (SNPs) and environmental variables linked to HCC in the Chinese population, which had been screened by meta-analyses. wGRS and PRS were built in training sets and validation sets. Area under the curve (AUC), net reclassification improvement (NRI), integrated discrimination improvement (IDI), Akaike information criterion (AIC), and Bayesian information criterion (BIC) were applied to evaluate the performance of the models. A total of 13 SNPs were included in both risk prediction models. Compared with wGRS, PRS had better accuracy and discrimination ability in predicting HCC risk. The AUC for PRS in combination with drinking history, cirrhosis, HBV infection, and family history of HCC in training sets and validation sets (AUC: 0.86, 95% CI: 0.84-0.89; AUC: 0.85, 95% CI: 0.81-0.89) increased at least 20% than the AUC for PRS alone (AUC: 0.63, 95% CI: 0.60-0.67; AUC: 0.65, 95% CI: 0.60-0.71). A novel model combining PRS with alcohol history, HBV infection, cirrhosis, and family history of HCC could be applied as an effective tool for risk prediction of HCC, which could discriminate at-risk individuals for precise prevention.

Quantification of large-spatial scale and in-stream factors affecting the structure of benthic macroinvertebrate communities in mountain streams

Habitat diversity plays a crucial role in shaping the structure and distribution of benthic macroinvertebrate communities. However, few studies have investigated the concurrent effects of such drivers operating at different spatial scales in mountain streams. Here, we assessed which environmental factors at different spatial scales, related to stream/river type and substrate, influence the diversity of benthic macroinvertebrate assemblages in mountain streams and rivers. Within the framework of the aquatic investigations of the Biodiversity Monitoring South Tyrol (BMS) – a newly-established, regional-based, long-term biodiversity monitoring program – benthic macroinvertebrates and a set of environmental factors describing water quality, stream/river hydromorphology and in-stream habitat characteristics were collected and analysed. The variation in total density, taxonomic richness, Shannon evenness and % Ephemeroptera–Plecoptera–Trichoptera taxa of stream benthic macroinvertebrates was assessed at multi-scale habitat levels, in 48 sampling sites across the mountainous region of the Autonomous Province of Bolzano/Bozen (Italy). The effects of stream/river type, substrates and different environmental factors on community composition of benthic macroinvertebrates were quantified, using a stepfoward Redundancy Analysis (RDA) and generalized additive models (GAMs). In addition, we performed an Indicator Value analysis to detect taxa that were significantly associated with a specific substrate and/or stream/river type. Overall, we observed significant differences in benthic biological metrics among both stream/river and substrate types. We also found at least one indicator taxa for each stream/river type, with environmental factors acting at reach and large-spatial scale – temperature, stream channel stability, elevation – being the key drivers in shaping macroinvertebrate distribution patterns in the analysed mountain rivers. While significant differences in macroinvertebrate community structures were found among substrates, the influence of stream/river type conditions was particularly evident. Apart from ranking environmental variables worth including in a long-term aquatic biodiversity monitoring program in mountain areas, our study provides a baseline for practical applications. For instance, depending on the desired outcome of habitat heterogeneity restoration, it can suggest the optimal scale (landscape vs. reach vs. patch) for prioritizing in-field interventions.

Assemblages of planktonic cnidarians in winter and their relationship to environmental conditions in the NW Mediterranean Sea

In the present study, we addressed the spatial characterization and species assemblages of the planktonic cnidarian community (Siphonophorae, Hydromedusae, and Scyphomedusae) in winter, a period that has been the subject of few studies in the NW Mediterranean. Data were obtained on two oceanographic cruises, in February 2017 and 2018. In 2017, the early onset of spring conditions and the subsequent phytoplankton bloom favored a mixture of winter and spring species, resulting in a higher species richness but a lower abundance of cnidarians. However, the typical winter oceanographic conditions in 2018 allowed winter species populations to develop, leading to a higher abundance of cnidarians that year. The most abundant species in both winters were Lensia subtilis, Muggiaea kochii, Chelophyes appendiculata, Abylopsis tetragona (eudoxid), Aglaura hemistoma, and Velella velella rataria larvae, while Obelia spp. was particularly numerous in 2017. In both years, the cluster and redundancy analyses showed a coastal-offshore ordination in species assemblages resulting from the effect of environmental variables (particularly bathymetry) and oceanographic structures (water masses and the shelf-slope density front). The presence of submarine canyons, in which great depths are reached close to the coast, modified the circulation patterns, resulting in a mixture of coastal and offshore species in these areas. In the current scenario of global warming, our results will help to provide a baseline for identifying future changes in the structure of the planktonic cnidarian community.

A dynamic outbreak distribution model (DODM) for an irruptive folivore: The western spruce budworm

Forest disturbances caused by irruptive insect outbreaks have become more frequent due to the non-stationary and warming climate that favours insect development and survival, as well as forest management practices that increase host tree susceptibility. However, predicting and managing the impacts of these highly mobile pests is a challenge due to their uncertain distributions. Dynamic species distribution models (DSDM) acknowledge the non-stationary environment and species’ ecological niches and facilitate niche modeling by matching species observational data relative to environmental data, leading to better outcomes and reducing over-prediction in conservation efforts. The western spruce budworm (WSB; Choristoneura freemani Freeman) is a native forest pest with irruptive dynamics characterized by periodic landscape-scale outbreaks. WSB feeds primarily on Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco) foliage and can cause extensive defoliation throughout western North American forests. Using decades-long presence and absence records of WSB outbreaks detected via aerial surveys and environmental variables, we developed dynamic outbreak distribution models (DODMs) to predict the distribution of outbreaks over time. Our results showed that DODMs provided robust temporal trends of WSB distributions while generating predictions that support documented northward shifts in epidemic populations. We found that in addition to the environmental variables known to affect WSB survival, phenological synchrony between bud burst and the emergence of larvae in spring was the most important predictor of outbreak distributions. We also identified high-risk areas for future WSB outbreaks and predicted range expansions and contractions under different climate change scenarios. Our study highlights the importance of using presence-absence data and incorporating temporal trends in environmental variables for better assessment of forest pest potential outbreak distributions. These findings have implications for proactive management strategies to prevent outbreaks and mitigate the impacts of WSB on forest ecosystems.

Using fossil records to predict short‐term changes in niche and spatial dynamics in a broadly distributed coral reef: Niche conservatism and adaptation

AbstractAimGiven the rapid pace of climate change and its significant impact on species distribution and survival, understanding the dynamics of ecological niches over time becomes imperative. By employing ecological niche models and species distribution models, alongside analyses of historical occurrence records and palaeoclimatic data, we aimed to assess the extent of changes in the niche of Porites astreoides from the Holocene to the present. Specifically, we quantified the amount of: (1) niche stability, (2) niche expansion, (3) niche decline and (4) niche overlap in both the environmental and geographical space.LocationAtlantic Ocean.TaxonCnidarian, Scleractinia.Materials and MethodsThrough fossil and current records and environmental variables from the same periods, we use two ecological niche modelling approaches (overlap and maximum entropy) to analyse niche dynamics in environmental and geographical space, specifically the degree of expansion, stability and loss of niches over the last ~8000 years. We used a principal component analysis to build the models within two different calibration areas (minimum convex polygon and buffer).ResultsOur results were similar, except for the loss of habitat, across the calibration areas used to build the models. The PCA used to build the models accounts for more than 65% of the variation in the data. Most of the environmental niche remained stable with an increase of almost a third and a negligible loss. Models in geographical space showed that most of the suitable area remained stable, but the species expanded its range from the Caribbean to the coast of Brazil. The analysis also showed a high value of niche overlap in environmental and geographical spaces.Main ConclusionsThe species studied is one of the corals with the greatest capacity to adapt to new environments and locations, but it also has a high tendency to preserve niche traits. These characteristics helped the species to expand its environmental and geographical niche in a period of relative climatic stability. On the other hand, these same characteristics of niche conservatism can lead the species to population decline and habitat loss, if the speed of adaptation is lower than the rapid climate changes predicted for the middle and end of the 21st century.

Analysis of small-scale soil CO2 fluxes in an orange orchard under irrigation and soil conservative practices

The quantification of soil carbon dioxide (CO2) flux represents an indicator of the agro-ecosystems sustainability. However, the monitoring of these fluxes is quite challenging due to their high spatially-temporally variability and dependence on environmental variables and soil management practices.In this study, soil CO2 fluxes were measured using a low-cost accumulation chamber, that was realized ad hoc for the surveys, in an orange orchard managed under different soil management (SM, bare versus mulched soils) and water regime (WR, full irrigation versus regulated deficit irrigation) strategies. In particular, the soil CO2 flux measurements were acquired in discontinuous and continuous modes, together with ancillary agrometeorological and soil-related information, and then compared to the agrosystem scale CO2 fluxes measured by the eddy covariance (EC) technique.Overall significant differences were obtained for the soil CO2 discontinuous fluxes as function of the WR (0.16 ± 0.01 and 0.14 ± 0.01 mg m−2 s−1 under full irrigation and regulated deficit irrigation, respectively). For the continuous soil CO2 measurements, the response observed for the SM factor varied from year to year, indicating for the overall reference period 2022-23 higher soil CO2 flux under the mulched soils (0.24 ± 0.01 mg m−2 s−1) than under bare soil conditions (0.15 ± 0.00 mg m−2 s−1). Inter-annual variations were also observed as function of the day-of-year (DOY), the SM and their interactions, resulting in higher soil CO2 flux under the mulched soils (0.24 ± 0.02 mg m−2 s−1) than under bare soil (0.15 ± 0.01 mg m−2 s−1) in certain periods of the years, according to the environmental conditions. Resultssuggest the importance of integrating soil CO2 flux measurements with ancillary variables that explain the variability of the agrosystem and the need to conduct the measurements using different operational modalities, also providing for night-time monitoring of CO2. In addition, the study underlines that the small-scale chamber measurements can be used to estimate soil CO2 fluxes at orchard scale if fluxes are properly scaled.

Improving the knowledge base of Vulnerable Marine Ecosystems’ distribution in the Amendolara Bank (Ionian Sea)

The Amendolara Bank was recently designated as a Site of Community Importance; however, information on its ecosystems remains limited, with a particular dearth of knowledge regarding the deeper zone (>100 m). Previous studies have discovered the existence of Vulnerable Marine Ecosystems (VMEs) within the bank, underscoring the necessity for more comprehensive data and its significance in terms of conservation in the Ionian Sea. This study aims to investigate various aspects of the bank using Remotely Operated Vehicles (ROVs) to shed light on the possible presence of VME indicator species, their relationship with environmental variables, their connection with the observed fish, and to assess anthropogenic impact via the identification of fishing gear within the study area. This present research provides new information on the biodiversity of the Amendolara Bank, revealing the presence of four VME indicator species including the first sightings of black coral Antipathella subpinnata and the precious red coral Corallium rubrum. Moreover, it provides evidence on the interaction between fish fauna and VME indicator species as well as signs of anthropogenic impact from abandoned, lost, or otherwise discarded fishing gear (ALDFG). These findings highlight the urgent need for additional research to improve the knowledge base on the biodiversity of this unique spot in the Ionian Sea which would in turn ensure the development and implementation of more effective conservation and management measures.

The Use of Boosted Regression Trees to Predict the Occurrence and Quantity of Staphylococcus aureus in Recreational Marine Waterways

Microbial monitoring in marine recreational waterways often overlooks environmental variables associated with pathogen occurrence. This study employs a predictive boosted regression trees (BRT) model to predict Staphylococcus aureus abundance in the Tampa Bay estuary and identify related environmental variables associated with the microbial pathogen’s occurrence. We provide evidence that the BRT model’s adaptability and ability to capture complex interactions among predictors make it invaluable for research on microbial indicator research. Over 18 months, water samples from 7 recreational sites underwent microbial quantitation and S. aureus isolation, followed by genetic validation. BRT analysis of S. aureus occurrence and environmental variables revealed month, precipitation, salinity, site, temperature, and year as relevant predictors. In addition, the BRT model accurately predicted S. aureus occurrence, setting a precedent for pathogen–environment research. The approach described here is novel and informs proactive management strategies and community health initiatives in marine recreational waterways.

부산시 카페 상권 분석; 매장이용 카페와 테이크아웃 카페 비교

The cafe business in Busan exhibits an annual growth trend, driven by an improving economic level and an increasing demand. Trends and consumption patterns in the cafe industry are subject to various influencing factors, and the volatility has intensified, particularly due to unexpected societal changes such as COVID-19. This study aims to analyze and predict trends in Busan's cafe business by considering population trends, the number of COVID cases, and the Consumer Price Index (CPI) of Busan. In particular, according to customer usage patterns, we will classify cafes as in-store cafe and takeout cafe, and analyze trends and forecasts of the number of cafes in a multivariate time series. The data used in this paper is collected and utilized from public data, and the open source Python programming language is used for analysis. The Busan cafe commercial district analysis method uses regression analysis, SARIMAX (Seasonal AutoRegressive Integrated Moving Average with eXogenous), and VAR (Vector Autoregression). Through this, we understand how the two types of cafes are affected by environmental variables and compare the performance and prediction ability of each analysis method. The results derived from this are expected to provide useful insights into the future development direction and prediction of the local cafe commercial area.

Regression Modelling of Flood Episodes in Ogbaru, Anambra State

Flooding has caused numerous damages to lives and properties. Ogbaru local government is a riverine area that was adversely affected by flooding due to the storm surges that over-flooded the riverbanks during the flooding incidents that affected most areas in Anambra State in the past. Thus, the aim of this study is to develop a regression model for flood events in Ogbaru Wetland, Anambra State. To achieve this, data was collected from the Nigerian Meteorological Agency's synoptic station (NIMET) for temperature, rainfall, relative humidity and the number of major flood episodes in the area for a40-year period (1971–2010). Multiple Linear Regression was used to analyse the data. The results obtained showed the values for Multiple R, R2, adjusted R2, standard error as 0.115618399, 0.013367614, -0.068851751, 1.836772885, 0.115618399, respectively. The correlation results presented relative humidity and rainfall as 0.757133219; number of flood events and rainfall as 0.107747581; number of flood events and relative humidity as 0.085395873. ANOVA used to determine the strength of the regression. Conclusively, it was observed that flooding in the area was caused by a combination of atmospheric variables that contributed roughly 1.34 percent. Also, anthropogenic and environmental variables were responsible for 98.66% of wetland flooding.

Modelling Habitat Suitability and Distribution of the Endemic Mindanao Horned Frog (Pelobatrachus stejnegeri) and its Response to Changing Climate

Climate change is already affecting biodiversity, with special concern to endemics whose range is restricted and limited. This study focuses on the Mindanao horned frog (Pelobatrachus stejnegeri), an endemic species to the Philippines, susceptible to climate-induced habitat changes. Using MaxEnt species distribution model (SDM), the current and future (year 2050 projections) habitat suitability and distribution of P. stejnegeri were modelled. Results showed that annual mean temperature, elevation, and annual precipitation were the environmental variables having the highest influence on P. stejnegeri's distribution. The model predicts a significant range contraction under representative concentration pathways (RCP) future scenarios (RCP 2.6 and RCP 8.5), with a more pronounced decrease in distribution (31.72%) under the high emission scenario (RCP 8.5). These findings emphasize the vulnerability of P. stejnegeri to climate change and highlight the importance of integrating SDM into conservation and management strategies to protect endemic species under changing climatic conditions.

PEMODELAN DISTRIBUSI KUCING TANDANG (Prionailurus planiceps) DI KAWASAN RESTORASI EKOSISTEM RIAU DI SEMENANJUNG KAMPAR, RIAU

The flat-headed cat is categorized as a threatened species by the International Union for the Conservation of Nature (IUCN). As the beginning of a conservation strategy for species that are difficult to find in nature, knowing the actual distribution of species in the field is important. Maximum entropy (Maxent) has emerged as an accurate prediction method for species distribution. This study aimed to analyze the distribution of flat-headed cats and important variables affecting their distribution in the Riau Ecosystem Restoration (RER) area on the Kampar Peninsula, Riau. A total of 6 finding points and 17 environmental variables were used in the modeling of this study. The Area Under Curve (AUC) value of the flat-headed cats was 0.890, indicating that the model performance was categorized as satisfactory. High habitat suitability was predicted ≥ 19% or (24,736.83 ha) of the total area. Environmental variables that influenced their distribution were distance from trails, distance from canals, and distance from rivers. The high suitability of the flat-headed cats’ habitat in the PT GCN concession is potentially along the Sangar River. In the PT SMN concession in the central and northwestern parts of the concession. PT TBOT concession is in areas adjacent to the Turip and Serkap Rivers. Although not detected in previous surveys, PT GAN has the potential to detect the presence of flat-headed cats in the eastern part and adjacent to the Serkap River.

Efficient calibration of cost-efficient particulate matter sensors using machine learning and time-series alignment

Atmospheric particulate matter (PM) poses a significant threat to human health, infiltrating the lungs and brain and leading to severe issues such as heart and lung diseases, cancer, and premature death. The main sources of PM pollution are vehicular and industrial emissions, construction and agricultural activities, and natural phenomena such as wildfires. Research underscores the absence of a safe threshold for particulate exposure, highlighting the crucial need for monitoring PM levels to develop and implement effective risk mitigation measures. Notwithstanding, accurate measurement of PM concentration relies on expensive and cumbersome equipment. Despite the rising popularity of low-cost alternatives, their reliability remains questionable, given their sensitivity to environmental conditions, inherent instability, and manufacturing imperfections. This article proposes a novel approach to efficient correction of low-cost PM sensors. The primary calibration model is a feedforward artificial neural network (ANN), which directly renders predicted output of the corrected sensor based on environmental variables such as temperature, humidity, and atmospheric pressure. The ANN hyper-parameters are identified by aligning time series of prior reference and low-cost sensor readings, which enables the network to learn typical temporal changes of the sensor outcome as a function of the aforementioned parameters as well as operational relationships between the sensor and the reference device. The architecture of the ANN is optimized in terms of the number of neurons in each layer to enhance its generalization capability. Our methodology has been demonstrated using a custom-designed portable monitoring platform and reference data acquired from public stations in Gdansk. The results are indicative of excellent calibration reliability. The achieved correlation coefficients w.r.t. the reference readings are 0.86, 0.88, and 0.72 for PM1, PM10, and PM2.5, respectively, whereas RMSE values are only 3.0, 3.9, and 5.4 µg/m³.