scholarly journals Impact of COVID-19 Social Distancing Policies on Traffic Congestion, Mobility, and NO2 Pollution

2021 ◽  
Vol 13 (13) ◽  
pp. 7275
Author(s):  
Alyse K. Winchester ◽  
Ryan A. Peterson ◽  
Ellison Carter ◽  
Mary D. Sammel
Keyword(s):  
Mixed Models ◽  
Traffic Congestion ◽  
Moving Average ◽  
Autoregressive Integrated Moving Average ◽  
Social Distancing ◽  
Pollution Levels ◽  
Moving Average Models ◽  
Us Cities ◽  
Percent Decrease ◽  
No2 Pollution

Lockdowns implemented during the COVID-19 pandemic were utilized to evaluate the associations between “social distancing policies” (SDPs), traffic congestion, mobility, and NO2 air pollution. Spatiotemporal linear mixed models were used on city-day data from 22 US cities to estimate the associations between SDPs, traffic congestion and mobility. Autoregressive integrated moving average models with Fourier terms were then used on historical data to forecast expected 2020 NO2. Time series models were subsequently employed to measure how much reductions in local traffic congestion were associated with lower-than-forecasted 2020 NO2. Finally, the equity of NO2 pollution was assessed with community-level sociodemographics. When cities’ most stringent SDPs were implemented, they observed a 23.47 (95% CI: 18.82–28.12) percent reduction in average daily congestion and a 13.48 (95% CI: 10.36–16.59) percent decrease in average daily mobility compared to unrestricted days. For each standard deviation (8.38%) reduction in local daily congestion, average daily NO2 decreased by 1.37 (95% CI: 1.24–1.51) parts per billion relative to its forecasted value. Citizenship, education, and race were associated with elevated absolute NO2 pollution levels but were not detectibly associated with reductions in 2020 NO2 relative to its forecasted value. This illustrates the immediate behavioral and environmental impacts of local SDPs during the COVID-19 pandemic.

scholarly journals Intervention analysis with autoregressive integrated moving average models for time series of urban air pollutants

2021 ◽  
Vol 2139 (1) ◽  
pp. 012002
Author(s):  
L A Manco-Perdomo ◽  
L A Pérez-Padilla ◽  
C A Zafra-Mejía
Keyword(s):  
Air Pollutants ◽  
Moving Average ◽  
Air Pollutant ◽  
Urban Air ◽  
Intervention Analysis ◽  
Autoregressive Integrated Moving Average ◽  
Pollutant Concentrations ◽  
Moving Average Models ◽  
Regulatory Interventions ◽  
Air Pollutant Concentrations

Abstract The objective of this paper is to show an intervention analysis with autoregressive integrated moving average models for time series of air pollutants in a Latin American megacity. The interventions considered in this study correspond to public regulations for the control of urban air quality. The study period comprised 10 years. Information from 10 monitoring stations distributed throughout the megacity was used. Modelling showed that setting maximum emission limits for different pollution sources and improving fuel were the most appropriate regulatory interventions to reduce air pollutant concentrations. Modelling results also suggested that these interventions began to be effective between the first 4 days-15 days after their publication. The models developed on a monthly timescale had a short autoregressive memory. The air pollutant concentrations at a given time were influenced by the concentrations of up to three months immediately preceding. Moving average term of the models showed fluctuations in time of the air pollutant concentrations (3 months - 14 months). Within the framework of the applications of physics for the air pollution control, this study is relevant for the following findings: the usefulness of autoregressive integrated moving average models to temporal simulate air pollutants, and for its suitable performance to detect and quantify regulatory interventions.

scholarly journals Transfer Function Analysis: Modelling Residential Building Costs in New Zealand by Including the Influences of House Price and Work Volume

Buildings ◽  
2019 ◽  
Vol 9 (6) ◽  
pp. 152
Author(s):  
Linlin Zhao ◽  
Jasper Mbachu ◽  
Zhansheng Liu ◽  
Huirong Zhang
Keyword(s):  
Time Series ◽  
New Zealand ◽  
Transfer Function ◽  
Moving Average ◽  
Cost Data ◽  
Autoregressive Integrated Moving Average ◽  
Work Volume ◽  
Moving Average Models ◽  
Function Models ◽  
Transfer Function Models

An accurate cost estimate not only plays a key role in project feasibility studies but also in achieving a final successful outcome. Conventionally, estimating cost typically relies on the experience of professionals and cost data from previous projects. However, this process is complex and time-consuming, and it is challenging to ensure the accuracy of the estimates. In this study, the bivariate and multivariate transfer function models were adopted to estimate and forecast the building costs of two types of residential buildings in New Zealand: Low-rise buildings and high-rise buildings. The transfer function method takes advantage of the merits of univariate time series analysis and the power of explanatory variables. In the dynamic project conduction environment, simply including building cost data in the cost forecasting models is not valid for making predictions, because the change in demand must be considered. Thus, the time series of house prices and work volume were used to explain exogenous effects in the transfer function model. To demonstrate the effectiveness of transfer function models, this study compared the results generated by the transfer function models with autoregressive integrated moving average models. According to the forecasting performance of the models, the proposed approach achieved better results than autoregressive integrated moving average models. The proposed method can provide accurate cost estimates that can help stakeholders in project budget planning and management strategy making at the early stage of a project.

scholarly journals Forecasting Rice Productivity and Production of Odisha, India, Using Autoregressive Integrated Moving Average Models

2014 ◽  
Vol 2014 ◽  
pp. 1-9
Author(s):  
Rahul Tripathi ◽  
A. K. Nayak ◽  
R. Raja ◽  
Mohammad Shahid ◽  
Anjani Kumar ◽  
...  
Keyword(s):  
Autocorrelation Function ◽  
Moving Average ◽  
Selection Criterion ◽  
Information Criteria ◽  
Arima Models ◽  
Autoregressive Integrated Moving Average ◽  
Rice Productivity ◽  
Rice Area ◽  
Moving Average Models ◽  
Partial Autocorrelation Function

Forecasting of rice area, production, and productivity of Odisha was made from the historical data of 1950-51 to 2008-09 by using univariate autoregressive integrated moving average (ARIMA) models and was compared with the forecasted all Indian data. The autoregressive (p) and moving average (q) parameters were identified based on the significant spikes in the plots of partial autocorrelation function (PACF) and autocorrelation function (ACF) of the different time series. ARIMA (2, 1, 0) model was found suitable for all Indian rice productivity and production, whereas ARIMA (1, 1, 1) was best fitted for forecasting of rice productivity and production in Odisha. Prediction was made for the immediate next three years, that is, 2007-08, 2008-09, and 2009-10, using the best fitted ARIMA models based on minimum value of the selection criterion, that is, Akaike information criteria (AIC) and Schwarz-Bayesian information criteria (SBC). The performances of models were validated by comparing with percentage deviation from the actual values and mean absolute percent error (MAPE), which was found to be 0.61 and 2.99% for the area under rice in Odisha and India, respectively. Similarly for prediction of rice production and productivity in Odisha and India, the MAPE was found to be less than 6%.

scholarly journals Evaluation of Seasonal Autoregressive Integrated Moving Average Models for River Flow Forecasting

2017 ◽  
Vol 13 (5) ◽  
pp. 378-387
Author(s):  
Kassahun Birhanu Tadesse ◽  
Megersa Olumana Dinka ◽  
Tena Alamirew ◽  
Semu Ayalew Moges
Keyword(s):  
River Flow ◽  
Moving Average ◽  
Autoregressive Integrated Moving Average ◽  
River Flow Forecasting ◽  
Moving Average Models
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