scholarly journals Development of an AI Model to Measure Traffic Air Pollution from Multisensor and Weather Data

Sensors ◽  
2019 ◽  
Vol 19 (22) ◽  
pp. 4941 ◽  
Author(s):  
Hai-Bang Ly ◽  
Lu Minh Le ◽  
Luong Van Phi ◽  
Viet-Hung Phan ◽  
Van Quan Tran ◽  
...  
Keyword(s):  
Air Pollution ◽  
Absolute Humidity ◽  
Absolute Error ◽  
Weather Data ◽  
Weather Variables ◽  
The Mean ◽  
Input Variables ◽  
Transport Emissions ◽  
The Impact ◽  
Quality Indexes

Gas multisensor devices offer an effective approach to monitor air pollution, which has become a pandemic in many cities, especially because of transport emissions. To be reliable, properly trained models need to be developed that combine output from sensors with weather data; however, many factors can affect the accuracy of the models. The main objective of this study was to explore the impact of several input variables in training different air quality indexes using fuzzy logic combined with two metaheuristic optimizations: simulated annealing (SA) and particle swarm optimization (PSO). In this work, the concentrations of NO2 and CO were predicted using five resistivities from multisensor devices and three weather variables (temperature, relative humidity, and absolute humidity). In order to validate the results, several measures were calculated, including the correlation coefficient and the mean absolute error. Overall, PSO was found to perform the best. Finally, input resistivities of NO2 and nonmetanic hydrocarbons (NMHC) were found to be the most sensitive to predict concentrations of NO2 and CO.

scholarly journals Investigating the impacts of air quality and weather indicators on the spread of SARS-COV-2 in Istanbul, Turkey

2021 ◽  
pp. 71-71
Author(s):  
Caner Taniş ◽  
Kadir Karakaya
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Meteorological Parameters ◽  
Positive Impact ◽  
Pearson Correlation ◽  
Quality Data ◽  
Hospital Discharges ◽  
Weather Parameters ◽  
The Impact ◽  
Quality Indexes

Background/aim: Air pollution is having a positive impact on the spread of the SARS-COV-2 virus. The effects of meteorological parameters on the spread of SARS-COV-2 are a matter of curiosity. The main purpose of this paper is to determine the association between air quality indexes (PM2.5, PM10, NO2, SO2, CO, and O3) and weather parameters (temperature, humidity, pressure, dew, wind speed) with the number of SARS-COV-2 cases, hospitalizations, hospital discharges. In this paper, we also focused on determining the impact of air pollution and weather parameters on the number of daily hospitalizations and daily discharges. Materials and methods: It is gleaned daily cases, hospitalizations, hospital discharges, meteorological, and air quality data in Istanbul from Turkey between July 15, 2020, and September 30, 2020. We performed the Pearson correlation analysis to evaluate the effects of meteorological parameters and air quality indexes on the variables related to SARS-COV-2. Results: It is determined a statistically significant positive relationship between air quality indexes such as CO, SO2, PM2.5, PM10, NO2, and the number of daily confirmed SARS-COV-2 cases. We also observed a negative association between weather parameters such as temperature and pressure and the number of daily confirmed SARS-COV-2 cases. Conclusion: Our study proposes that high air quality could reduce the number of SARS-COV-2 cases. The empirical findings of this paper might provide key input to prevent the spread of SARS-COV-2 across Turkey.

scholarly journals Impact of Carriage Crowding Level on Bus Dwell Time: Modelling and Analysis

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Yiming Bie ◽  
Yunhao Wang ◽  
Le Zhang
Keyword(s):  
Dwell Time ◽  
Mean Absolute Error ◽  
Time Estimation ◽  
Absolute Error ◽  
Regression Method ◽  
Travel Time Estimation ◽  
The Mean ◽  
Bus Schedule ◽  
The Impact ◽  
Estimation Models

This paper develops two types of estimation models to quantify the impacts of carriage crowding level on bus dwell time. The first model (model I) takes the crowding level and the number of alighting and boarding passengers into consideration and estimates the alighting time and boarding time, respectively. The second model (model II) adopts almost the same regression method, except that the impact of crowding on dwell time is neglected. The analysis was conducted along two major bus routes in Harbin, China, by collecting 640 groups of dwell times under crowded condition manually. Compared with model II, the mean absolute error (MAE) of model I is reduced by 137.51%, which indicates that the accuracy of bus dwell time estimation could be highly improved by introducing carriage crowding level into the model. Meanwhile, the MAE of model I is about 3.9 seconds, which is acceptable in travel time estimation and bus schedule.

scholarly journals The Impact of the Lockdown Caused by the COVID-19 Pandemic on the Fine Particulate Matter (PM2.5) Air Pollution: The Greek Paradigm

2021 ◽  
Vol 18 (13) ◽  
pp. 6748
Author(s):  
Ourania S. Kotsiou ◽  
Georgios K. D. Saharidis ◽  
Georgios Kalantzis ◽  
Evangelos C. Fradelos ◽  
Konstantinos I. Gourgoulianis
Keyword(s):  
Air Pollution ◽  
Wireless Sensors ◽  
Fine Particulate Matter ◽  
Meteorological Conditions ◽  
Significant Drop ◽  
Fine Particulate ◽  
Guideline Value ◽  
The Mean ◽  
The Impact ◽  
Pm2.5 Concentration

Introduction: Responding to the coronavirus pandemic, Greece implemented the largest quarantine in its history. No data exist regarding its impact on PM2.5 pollution. We aimed to assess PM2.5 levels before, during, and after lockdown (7 March 2020–16 May 2020) in Volos, one of Greece’s most polluted industrialized cities, and compare PM2.5 levels with those obtained during the same period last year. Meteorological conditions were examined as confounders. Methods: The study period was discriminated into three phases (pre-lockdown: 7 March–9 March, lockdown: 10 March–4 May, and post-lockdown period: 5 May–16 May). A wireless sensors network was used to collect PM2.5, temperature, relative humidity, rainfall, and wind speed data every 2 s. Results: The lockdown resulted in a significant drop of PM2.5 by 37.4% in 2020, compared to 2019 levels. The mean daily concentrations of PM2.5 exceeded the WHO’s guideline value for 24-h mean levels of PM2.5 35% of the study period. During the strictest lockdown (23 March to 4 May), the mean daily PM2.5 levels exceeded the standard 41% of the time. The transition from the pre-lockdown period into lockdown or post-lockdown periods was associated with lower PM2.5 concentrations. Conclusions: A reduction in the mean daily PM2.5 concentration was found compared to 2019. Lockdown was not enough to avoid severe exceedances of air pollution in Volos.

scholarly journals Moisture in New Zealand Bathrooms - Analysing Moisture Events

2021 ◽  
Author(s):  
◽  
Alister Stubbe
Keyword(s):  
New Zealand ◽  
Indoor Environment ◽  
Rapid Change ◽  
Absolute Humidity ◽  
Building Code ◽  
Large Impact ◽  
The Mean ◽  
Typical Day ◽  
House Condition ◽  
The Impact

<p>A literature review was carried out on the impact of moisture in New Zealand homes as well as the role ventilation and occupant behaviour play in controlling this. Bathrooms in residential homes were identified as being especially vulnerable. NZS4303:1990, clause G4 Ventilation of the New Zealand Building Code, and clause E3 of the New Zealand Building Code were summarised to provide context for how New Zealand buildings are designed.  Measurements taken in houses throughout New Zealand by BRANZ as part of the House Condition Survey were made available for analysis. This included measurements of relative humidity and temperature.  Data from one Dunedin house was thoroughly explored. This involved three objectives. The first step focused on identifying periods of rapid change in the amount of moisture introduced to the indoor environment, measured in absolute humidity. These periods were named 'moisture events'. The second objective was to visually communicate the changes in temperature and absolute humidity taking place on individual days, highlighting moisture events. The third objective was to analyse the identified moisture events, finding the key areas to focus on for the full analysis as well as areas that could be explored in further research. This process was then applied to all remaining houses.  Moisture events were grouped into four categories: increases, decreases, episodes, and combinations. Episodes were the focus of the analysis, representing moisture being actively introduced to the indoor environment and then removed. These categories were further filtered, identifying the moisture events were most likely to have had a large impact on the indoor environment. Days were broken into four hour periods, with the filtered moisture events taking place in each period recorded. These were used to identify patterns in moisture events for each house. If a certain pattern of moisture events frequently took place, then days containing that pattern were described as a 'typical day' for that house.  The mean and median absolute humidity at the start, peak, and end of the unfiltered episodes from each house were then calculated. The mean and median episode length was also calculated. The results were compared to the Household Energy End-use Project (HEEP) and to the typical days for each house. The results were grouped according to factors such as the number of bathrooms in the house, the floor area, the house location, and the event length.  The number of bathrooms present in the house was found to have a large impact on the size and frequency of moisture events. As expected, larger bathrooms recorded lower increases in absolute humidity from the start to the peak of episodes. Rooms with a greater volume would require more moisture to reach the same number of grams of water per cubic metre. However, the smallest bathrooms also recorded low increases in absolute humidity.</p>

scholarly journals Forecasting of Heat Production in Combined Heat and Power Plants Using Generalized Additive Models

Energies ◽  
2021 ◽  
Vol 14 (8) ◽  
pp. 2331
Author(s):  
Maciej Bujalski ◽  
Paweł Madejski
Keyword(s):  
Heat Production ◽  
Heat Load ◽  
Combined Heat And Power ◽  
Additive Models ◽  
Measuring System ◽  
Weather Data ◽  
Data Set ◽  
District Heating System ◽  
Input Variables ◽  
The Impact

The paper presents a developed methodology of short-term forecasting for heat production in combined heat and power (CHP) plants using a big data-driven model. An accurate prediction of an hourly heat load in the day-ahead horizon allows a better planning and optimization of energy and heat production by cogeneration units. The method of training and testing the predictive model with the use of generalized additive model (GAM) was developed and presented. The weather data as an input variables of the model were discussed to show the impact of weather conditions on the quality of predicted heat load. The new approach focuses on an application of the moving window with the learning data set from the last several days in order to adaptively train the model. The influence of the training window size on the accuracy of forecasts was evaluated. Different versions of the model, depending on the set of input variables and GAM parameters were compared. The results presented in the paper were obtained using a data coming from the real district heating system of a European city. The accuracy of the methods during the different periods of heating season was performed by comparing heat demand forecasts with actual values, coming from a measuring system located in the case study CHP plant. As a result, a model with an averaged percentage error for the analyzed period (November–March) of less than 7% was obtained.

scholarly journals Short-Term Load Forecasting Using Convolutional Neural Networks in COVID-19 Context: The Romanian Case Study

Energies ◽  
2021 ◽  
Vol 14 (13) ◽  
pp. 4046
Author(s):  
Andrei M. Tudose ◽  
Irina I. Picioroaga ◽  
Dorian O. Sidea ◽  
Constantin Bulac ◽  
Valentin A. Boicea
Keyword(s):  
Power Systems ◽  
Mean Squared Error ◽  
Load Forecasting ◽  
Absolute Error ◽  
Percentage Error ◽  
Linear Regression Method ◽  
Short Term ◽  
Short Term Load Forecasting ◽  
The Mean ◽  
The Impact

Short-term load forecasting (STLF) is fundamental for the proper operation of power systems, as it finds its use in various basic processes. Therefore, advanced calculation techniques are needed to obtain accurate results of the consumption prediction, taking into account the numerous exogenous factors that influence the results’ precision. The purpose of this study is to integrate, additionally to the conventional factors (weather, holidays, etc.), the current aspects regarding the global COVID-19 pandemic in solving the STLF problem, using a convolutional neural network (CNN)-based model. To evaluate and validate the impact of the new variables considered in the model, the simulations are conducted using publicly available data from the Romanian power system. A comparison study is further carried out to assess the performance of the proposed model, using the multiple linear regression method and load forecasting results provided by the Romanian Transmission System Operator (TSO). In this regard, the Mean Squared Error (MSE), the Mean Absolute Error (MAE), the Mean Absolute Percentage Error (MAPE), and the Root Mean Square Error (RMSE) are used as evaluation indexes. The proposed methodology shows great potential, as the results reveal better error values compared to the TSO results, despite the limited historical data.

scholarly journals Impact of the Mean Daily Air Temperature Calculation on the Rainfall-Runoff Modelling

Water ◽  
2020 ◽  
Vol 12 (11) ◽  
pp. 3175
Author(s):  
Nejc Bezak ◽  
Lazar Cerović ◽  
Mojca Šraj
Keyword(s):  
Air Temperature ◽  
Input Data ◽  
Absolute Error ◽  
Rainfall Runoff ◽  
Rating Curve ◽  
Discharge Data ◽  
Temperature Calculation ◽  
Maximum Discharge ◽  
The Mean ◽  
The Impact

Conceptual rainfall-runoff models besides precipitation and discharge data generally require estimates of the mean daily air temperature as input data. For the estimation of the mean daily air temperature, there are different methods available. The paper presents an evaluation of the impact of the mean daily air temperature calculation on the rainfall-runoff modelling results. Additionally, other measured variables and rating curve uncertainty were assessed. Differences in the mean daily air temperature values were evaluated for the 33 meteorological stations in Slovenia and additional investigations were conducted for four selected meso-scale catchments located in different climates. The results of the application of four equations for the mean air temperature calculation yielded the mean absolute error values between 0.56–0.80 °C. However, the results of rainfall-runoff modelling showed that these differences had an almost negligible impact on the model results. Differences in the mean simulated discharge values were no larger than 1%, while differences in the maximum discharge values were a bit larger, but did not exceed 5%. A somewhat larger impact on the model results was observed when precipitation and water level measurements’ uncertainty was included. However, among all analysed input data uncertainties, the rating curve uncertainty can be regarded as the most influential with differences in the simulated mean discharge values in the range of 3% and differences in the maximum discharge values up to 14%.

scholarly journals Forecasting PM10 in the Bay of Algeciras Based on Regression Models

2019 ◽  
Vol 11 (4) ◽  
pp. 968 ◽  
Author(s):  
José Palomares-Salas ◽  
Juan González-de-la-Rosa ◽  
Agustín Agüera-Pérez ◽  
José Sierra-Fernández ◽  
Olivia Florencias-Oliveros
Keyword(s):  
Mean Squared Error ◽  
Absolute Error ◽  
Average Concentration ◽  
Improve Performance ◽  
Urban Centers ◽  
Experimental Procedure ◽  
Additional Information ◽  
Squared Error ◽  
The Mean ◽  
Quality Indexes

Different forecasting methodologies, classified into parametric and nonparametric, were studied in order to predict the average concentration of P M 10 over the course of 24 h. The comparison of the forecasting models was based on four quality indexes (Pearson’s correlation coefficient, the index of agreement, the mean absolute error, and the root mean squared error). The proposed experimental procedure was put into practice in three urban centers belonging to the Bay of Algeciras (Andalusia, Spain). The prediction results obtained with the proposed models exceed those obtained with the reference models through the introduction of low-quality measurements as exogenous information. This proves that it is possible to improve performance by using additional information from the existing nonlinear relationships between the concentration of the pollutants and the meteorological variables.

scholarly journals Moisture in New Zealand Bathrooms - Analysing Moisture Events

2021 ◽  
Author(s):  
◽  
Alister Stubbe
Keyword(s):  
New Zealand ◽  
Indoor Environment ◽  
Rapid Change ◽  
Absolute Humidity ◽  
Building Code ◽  
Large Impact ◽  
The Mean ◽  
Typical Day ◽  
House Condition ◽  
The Impact

<p>A literature review was carried out on the impact of moisture in New Zealand homes as well as the role ventilation and occupant behaviour play in controlling this. Bathrooms in residential homes were identified as being especially vulnerable. NZS4303:1990, clause G4 Ventilation of the New Zealand Building Code, and clause E3 of the New Zealand Building Code were summarised to provide context for how New Zealand buildings are designed.  Measurements taken in houses throughout New Zealand by BRANZ as part of the House Condition Survey were made available for analysis. This included measurements of relative humidity and temperature.  Data from one Dunedin house was thoroughly explored. This involved three objectives. The first step focused on identifying periods of rapid change in the amount of moisture introduced to the indoor environment, measured in absolute humidity. These periods were named 'moisture events'. The second objective was to visually communicate the changes in temperature and absolute humidity taking place on individual days, highlighting moisture events. The third objective was to analyse the identified moisture events, finding the key areas to focus on for the full analysis as well as areas that could be explored in further research. This process was then applied to all remaining houses.  Moisture events were grouped into four categories: increases, decreases, episodes, and combinations. Episodes were the focus of the analysis, representing moisture being actively introduced to the indoor environment and then removed. These categories were further filtered, identifying the moisture events were most likely to have had a large impact on the indoor environment. Days were broken into four hour periods, with the filtered moisture events taking place in each period recorded. These were used to identify patterns in moisture events for each house. If a certain pattern of moisture events frequently took place, then days containing that pattern were described as a 'typical day' for that house.  The mean and median absolute humidity at the start, peak, and end of the unfiltered episodes from each house were then calculated. The mean and median episode length was also calculated. The results were compared to the Household Energy End-use Project (HEEP) and to the typical days for each house. The results were grouped according to factors such as the number of bathrooms in the house, the floor area, the house location, and the event length.  The number of bathrooms present in the house was found to have a large impact on the size and frequency of moisture events. As expected, larger bathrooms recorded lower increases in absolute humidity from the start to the peak of episodes. Rooms with a greater volume would require more moisture to reach the same number of grams of water per cubic metre. However, the smallest bathrooms also recorded low increases in absolute humidity.</p>

scholarly journals Impacts of Weather on Short-Term Metro Passenger Flow Forecasting Using a Deep LSTM Neural Network

2020 ◽  
Vol 10 (8) ◽  
pp. 2962
Author(s):  
Lijuan Liu ◽  
Rung-Ching Chen ◽  
Shunzhi Zhu
Keyword(s):  
Neural Network ◽  
Short Term Memory ◽  
Weather Conditions ◽  
Urban Transport ◽  
Weather Data ◽  
Weather Variables ◽  
Short Term ◽  
Passenger Flow ◽  
Input Variables ◽  
Main Station

Metro systems play a key role in meeting urban transport demands in large cities. The close relationship between historical weather conditions and the corresponding passenger flow has been widely analyzed by researchers. However, few studies have explored the issue of how to use historical weather data to make passenger flow forecasting more accurate. To this end, an hourly metro passenger flow forecasting model using a deep long short-term memory neural network (LSTM_NN) was developed. The optimized traditional input variables, including the different temporal data and historical passenger flow data, were combined with weather variables for data modeling. A comprehensive analysis of the weather impacts on short-term metro passenger flow forecasting is discussed in this paper. The experimental results confirm that weather variables have a significant effect on passenger flow forecasting. It is interesting to find out that the previous variables of one-hour temperature and wind speed are the two most important weather variables to obtain more accurate forecasting results on rainy days at Taipei Main Station, which is a primary interchange station in Taipei. Compared to the four widely used algorithms, the deep LSTM_NN is an extremely powerful method, which has the capability of making more accurate forecasts when suitable weather variables are included.

Sign in / Sign up

Export Citation Format

Share Document