The Role of Temperature and Air Quality in Outdoor Recreation Behavior: A Social-Ecological Systems Approach

Zajchowski, Chris A. B.; South, Forrest; Rose, Jeff; Crofford, Eleanor

doi:10.1080/00167428.2021.1897811

Cited by 5 publications

(2 citation statements)

References 64 publications

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“…The pervasiveness of air pollution in numerous global cities poses significant threats to public health and induces sustained detrimental effects on broader ecological systems [1][2][3]. Among the various pollutants, fine particulate matter (PM 2.5 ), with a diameter less than 2.5 µm, stands out [4,5]. Its adverse effects range from respiratory to cardiovascular diseases, underscoring the importance of developing a model that can accurately predict PM 2.5 concentrations, allowing for timely prevention measures and strategic responses [6][7][8][9].…”

Section: Introductionmentioning

confidence: 99%

MSAFormer: A Transformer-Based Model for PM2.5 Prediction Leveraging Sparse Autoencoding of Multi-Site Meteorological Features in Urban Areas

Wang

Zhang

2023

Atmosphere

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The accurate prediction of PM2.5 concentration, a matter of paramount importance in environmental science and public health, has remained a substantial challenge. Conventional methodologies for predicting PM2.5 concentration often grapple with capturing complex dynamics and nonlinear relationships inherent in multi-station meteorological data. To address this issue, we have devised a novel deep learning model, named the Meteorological Sparse Autoencoding Transformer (MSAFormer). The MSAFormer leverages the strengths of the Transformer architecture, effectively incorporating a Meteorological Sparse Autoencoding module, a Meteorological Positional Embedding Module, and a PM2.5 Prediction Transformer Module. The Sparse Autoencoding Module serves to extract salient features from high-dimensional, multi-station meteorological data. Subsequently, the Positional Embedding Module applies a one-dimensional Convolutional Neural Network to flatten the sparse-encoded features, facilitating data processing in the subsequent Transformer module. Finally, the PM2.5 Prediction Transformer Module utilizes a self-attention mechanism to handle temporal dependencies in the input data, predicting future PM2.5 concentrations. Experimental results underscore that the MSAFormer model achieves a significant improvement in predicting PM2.5 concentrations in the Haidian district compared to traditional methods. This research offers a novel predictive tool for the field of environmental science and illustrates the potential of deep learning in the analysis of environmental meteorological data.

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Section: Introductionmentioning

confidence: 99%

MSAFormer: A Transformer-Based Model for PM2.5 Prediction Leveraging Sparse Autoencoding of Multi-Site Meteorological Features in Urban Areas

Wang

Zhang

2023

Atmosphere

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“…An increase in greenhouse gas concentration will lead to changes in the temperature and other climate factors [15][16][17], thus impacting the individuals' decisions on outdoor recreations. While it is easy to agree that climate change will impact outdoor recreations, current research provides mostly qualitative evidence and speculates about the relationship or limits to specific types of recreation activities such as bicycling or fishing [18][19][20][21][22][23][24]. This paper advances the literature by providing the first comprehensive quantitative analyses on the relationship between temperature and outdoor recreation activities based on more than 30 million individual-level trip data obtained from Recreation.gov, a portal for outside recreation activities at federal sites collaborated by 12 federal agencies such as the National Park Service.…”

Section: Introductionmentioning

confidence: 99%

The effect of temperature on outdoor recreation activities: evidence from visits to federal recreation sites

Liu

2022

Environ. Res. Lett.

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This paper uses over 30 million individual-level trips in federal recreation locations to investigate the impact of short-term temperature shocks on outdoor recreation activities. Our results show that in the short term, a 1◦C temperature increase during the last 6 months increases the total trip duration by 1.197 days (or a 4.12% increase) and the total number of trips by 0.472 (or a 5.44% increase) at the zipcode-month level. The positive effect is primarily driven by the increased number of trips and more in- state travel. We find that the impact of temperature on the number of recreation trips generally increases under a higher temperature. When the monthly temperature is below < 5◦C, the temperature increase will reduce the number of trips as individuals in low-temperature regions is likely to reduce travel demand when the temperature gets warmer.

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Investigation of impact of community environment factors on the residents’ visiting rate to outdoor venues in South China

Feng,

Lou,

Zou

et al. 2024

City Built Enviro

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Understanding the activity patterns of urban residents is crucial for urban community design and urban energy forecasting. Predicting the visitation rates of different types of locations is a important issue because urban residents can only be in one place at a time. Achieving accurate predictions of outdoor public space visitation rates is equivalent to predicting the upper limit of the number of visitors to other buildings. Previous similar studies usually rely on survey methods such as questionnaires and GPS positioning. These methods have some drawbacks, such as small sample sizes and inaccurate data. In this paper, we used mobile signaling to investigate the park visit rates of residents in different communities in Guangzhou. In accordance with Chinese tradition, we define a community as a residential community, which consists of multiple residential units and a set of basic service facilities and management structures that are sufficient to meet the daily needs of residents. We found that the park visit rate of a community is closely related to the characteristics of the community itself. The park visit rate is positively correlated with the average LST (land surface temperature) of the communities, the average distance between the community and the large park, and the building coverage rate, while it is negatively correlated with the vegetation coverage rate, the average surface reflection rate, the convenience of transportation, and the per capita green space area. Given the different travel habits of urban residents on weekdays and weekends, we also used multiple linear regression to establish prediction models for community park visitation rates on weekdays and weekends.

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The Role of Temperature and Air Quality in Outdoor Recreation Behavior: A Social-Ecological Systems Approach

Cited by 5 publications

References 64 publications

MSAFormer: A Transformer-Based Model for PM2.5 Prediction Leveraging Sparse Autoencoding of Multi-Site Meteorological Features in Urban Areas

MSAFormer: A Transformer-Based Model for PM2.5 Prediction Leveraging Sparse Autoencoding of Multi-Site Meteorological Features in Urban Areas

The effect of temperature on outdoor recreation activities: evidence from visits to federal recreation sites

Investigation of impact of community environment factors on the residents’ visiting rate to outdoor venues in South China

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