Space-time mapping of ground-level PM2.5 and NO2 concentrations in heavily polluted northern China during winter using the Bayesian maximum entropy technique with satellite data

2017 ◽  
Vol 11 (1) ◽  
pp. 23-33 ◽  
Author(s):  
Qutu Jiang ◽  
George Christakos
Keyword(s):  
Maximum Entropy ◽  
Satellite Data ◽  
Ground Level ◽  
Northern China ◽  
Space Time ◽  
Bayesian Maximum Entropy ◽  
Entropy Technique

scholarly journals Hierarchical Bayesian space-time interpolation versus spatio-temporal BME approach

2010 ◽  
Vol 25 ◽  
pp. 97-102 ◽  
Author(s):  
I. Hussain ◽  
J. Pilz ◽  
G. Spoeck
Keyword(s):  
Maximum Entropy ◽  
Time Domain ◽  
Prediction Error ◽  
Bayesian Model ◽  
Space Time ◽  
Bayesian Maximum Entropy ◽  
Hierarchical Bayesian ◽  
Hierarchical Bayesian Model ◽  
Monsoon Period ◽  
Spatio Temporal

Abstract. The restrictions of the analysis of natural processes which are observed at any point in space or time to a purely spatial or purely temporal domain may cause loss of information and larger prediction errors. Moreover, the arbitrary combinations of purely spatial and purely temporal models may not yield valid models for the space-time domain. For such processes the variation can be characterized by sophisticated spatio-temporal modeling. In the present study the composite spatio-temporal Bayesian maximum entropy (BME) method and transformed hierarchical Bayesian space-time interpolation are used in order to predict precipitation in Pakistan during the monsoon period. Monthly average precipitation data whose time domain is the monsoon period for the years 1974–2000 and whose spatial domain are various regions in Pakistan are considered. The prediction of space-time precipitation is applicable in many sectors of industry and economy in Pakistan especially; the agricultural sector. Mean field maps and prediction error maps for both methods are estimated and compared. In this paper it is shown that the transformed hierarchical Bayesian model is providing more accuracy and lower prediction error compared to the spatio-temporal Bayesian maximum entropy method; additionally, the transformed hierarchical Bayesian model also provides predictive distributions.

scholarly journals When a Generalized Linear Model Meets Bayesian Maximum Entropy: A Novel Spatiotemporal Ground-Level Ozone Concentration Retrieval Method

2021 ◽  
Vol 13 (21) ◽  
pp. 4324
Author(s):  
Yingying Mei ◽  
Jiayi Li ◽  
Deping Xiang ◽  
Jingxiong Zhang
Keyword(s):  
Maximum Entropy ◽  
Hybrid Model ◽  
Large Scale ◽  
Ground Level ◽  
Superior Performance ◽  
Spatiotemporal Variability ◽  
Coefficient Of Determination ◽  
Bayesian Maximum Entropy ◽  
Regional Patterns ◽  
Ground Level Ozone

In China, ground-level ozone has shown an increasing trend and has become a serious ambient pollutant. An accurate spatiotemporal distribution of ground-level ozone concentrations (GOCs) is urgently needed. Generalized linear models (GLMs) and Bayesian maximum entropy (BME) models are practical for predicting GOCs. However, GLMs have limited capacity to capture temporal variations and can miss some short-term and regional patterns, while the performance of BME models may degrade in cases of sparse or imperfect monitoring networks. Thus, to predict nationwide 1 km monthly average GOCs for China, we designed a novel hybrid model containing three modules. (1) A GLM was established to accurately describe the variability in GOCs in the space domain. (2) A BME model incorporating GLM residuals was employed to capture the temporal variability of GOCs in detail. (3) A combination of GLM and BME models was developed based on the specific broad range of each submodel. According to the cross-validation results, the hybrid model exhibited superior performance, with coefficient of determination (R2) values of 0.67. The predictive performance of the large-scale and high-resolution hybrid model is superior to that in previous studies. The nationwide spatiotemporal variability of the GOCs derived from the hybrid model shows that they are valuable indicators for ground-level ozone pollution control and prevention in China.

Sign in / Sign up

Export Citation Format

Share Document