Climatology and model prediction of aerosol optical properties over the Indo-Gangetic Basin in north India.

The current research focuses on the use of different simulation techniques in the future prediction of the crucial aerosol optical properties over the highly polluted Indo-Gangetic Basin in the northern part of India. The time series model was used to make an accurate forecast of aerosol optical depth (AOD) and angstrom exponent (AE), and the statistical variability of both cases was compared in order to evaluate the effectiveness of the model (training and validation). For this, different models were used to simulate the monthly average AOD and AE over Jaipur, Kanpur and Ballia during the period from 2003 to 2018. Further, the study was aimed to construct a comparative model that will be used for time series statistical analysis of MODIS-derived AOD550 and AE412-470. This will provide a more comprehensive information about the levels of AOD and AE that will exist in the future. To test the validity and applicability of the developed models, root-mean-square error (RMSE), mean absolute error (MAE), mean absolute percent error (MAPE), fractional bias (FB), and Pearson coefficient (r) were used to show adequate accuracy in model performance. From the observation, the monthly mean values of AOD and AE were found to be nearly similar at Kanpur and Ballia (0.62 and 1.26) and different at Jaipur (0.25 and 1.14). Jaipur indicates that during the pre-monsoon season, the AOD mean value was found to be highest (0.32 ± 0.15), while Kanpur and Ballia display higher AOD mean values during the winter season (0.72 ± 0.26 and 0.83 ± 0.32, respectively). Among the different methods, the autoregressive integrated moving average (ARIMA) model was found to be the best-suited model for AOD prediction at Ballia based on fitted error (RMSE (0.22), MAE (0.15), MAPE (24.55), FB (0.05)) and Pearson coefficient r (0.83). However, for AE, best prediction was found at Kanpur based on RMSE (0.24), MAE (0.21), MAPE (22.54), FB (-0.09) and Pearson coefficient r (0.82).

Rethinking health planning: a framework for organising information to underpin collaborative health planning.

The field of collaborative health planning faces significant challenges created by the narrow focus of the available information, the absence of a framework to organise that information and the lack of systems to make information accessible and guide decision-making. These challenges have been magnified by the rise of the 'healthy communities movement', resulting in more frequent calls for localised, collaborative and evidence-driven health related decision-making. This paper discusses the role of decision support systems as a mechanism to facilitate collaborative health decision-making. The paper presents a potential information management framework to underpin a health decision support system and describes the participatory process that is currently being used to create an online tool for health planners using geographic information systems. The need for a comprehensive information management framework to guide the process of planning for healthy communities has been emphasised. The paper also underlines the critical importance of the proposed framework not only in forcing planners to engage with the entire range of health determinants, but also in providing sufficient flexibility to allow exploration of the local setting-based determinants of health.