Bridging research-policy gaps: An integrated approach.

It is often difficult for policymakers to make informed decisions without evidence-based support, resulting in potentially ineffective policies. The purpose of this article is to advocate for collaboration and communication between researchers and policymakers to enhance evidence-based policymaking. The workshop hosted by the International Society of Environmental Epidemiology-Eastern Mediterranean Chapter further explores the challenges of connecting researchers and policymakers. The article highlights the gap between researchers and policymakers, attributed to different visions and objectives, time constraints, and communication issues. To strengthen the research-policy interface, strategies such as enhanced communication skills and early involvement of policymakers in research are suggested. The article proposes an integrated model combining the Collaborative Knowledge Model and the policy entrepreneurship mindset, emphasizing the co-creation of knowledge and evidence-based policy solutions. The use of this model can lead to the development of evidence-based policies that effectively address societal needs.

A new hybrid models based on the neural network and discrete wavelet transform to identify the CHIMERE model limitation.

A greater understanding of ozone damage to the environment and health led to an increased demand for accurate predictions. This study provides two new accurate hybrid models of ozone prediction. The first one (CHIMERE-NARX) is based on a NARX model as a post-processing of the CHIMERE model. In the second (CHIMERE-NARX-DWT), a discrete wavelet transform (DWT) has been added. Our models were built and validated using ozone measurements from the Mediouna station in Casablanca, Morocco, from February 1st to March 27th, 2021. The results highlighted the CHIMERE model limitations, such as wind speed overestimation and insufficient emission data. The first hybrid successfully increased the correlation coefficient from 88 to 93% and reduced RMSE from 23.99 µg/m3 to -3.54 µg/m3, overcoming CHIMERE limitations to some extent, especially during nighttime. A second hybrid addressed the first hybrid limitation, such as using ozone as a single input. This hybrid successfully balanced the weight of NARX at night against the day, increasing the correlation coefficient to 98% and decreasing RMSE to -0.02 µg/m3. This study presents a new generation of post-processing based on deterministic model processes, with the possibility of training them with minimum input data, which can be applied to other models using various pollutants.

Relationship between Meteorological and Air Quality Parameters and COVID-19 in Casablanca Region, Morocco.

The aim of this study was to investigate the relationship between meteorological parameters, air quality and daily COVID-19 transmission in Morocco. We collected daily data of confirmed COVID-19 cases in the Casablanca region, as well as meteorological parameters (average temperature, wind, relative humidity, precipitation, duration of insolation) and air quality parameters (CO, NO2, 03, SO2, PM10) during the period of 2 March 2020, to 31 December 2020. The General Additive Model (GAM) was used to assess the impact of these parameters on daily cases of COVID-19. A total of 172,746 confirmed cases were reported in the study period. Positive associations were observed between COVID-19 and wind above 20 m/s and humidity above 80%. However, temperatures above 25° were negatively associated with daily cases of COVID-19. PM10 and O3 had a positive effect on the increase in the number of daily confirmed COVID-19 cases, while precipitation had a borderline effect below 25 mm and a negative effect above this value. The findings in this study suggest that significant associations exist between meteorological factors, air quality pollution (PM10) and the transmission of COVID-19. Our findings may help public health authorities better control the spread of COVID-19.

Concurrent Heat Waves and Extreme Ozone (O3) Episodes: Combined Atmospheric Patterns and Impact on Human Health.

More recurrent heat waves and extreme ozone (O3) episodes are likely to occur during the next decades and a key question is about the concurrence of those hazards, the atmospheric patterns behind their appearance, and their joint effect on human health. In this work, we use surface maximum temperature and O3 observations during extended summers in two cities from Morocco: Casablanca and Marrakech, between 2010 and 2019. We assess the connection between these data and climate indices (North Atlantic Oscillation (NAO), Mediterranean Oscillation (MO), and Saharan Oscillation (SaO)). We then identify concurrent heat waves and O3 episodes, the weather type behind this concurrence, and the combined health risks. Our findings show that the concurrence of heat waves and O3 episodes depends both on the specific city and the large-scale atmospheric circulation. The likely identified synoptic pattern is when the country is under the combined influence of an anticyclonic area in the north and the Saharan trough extending the depression centered in the south. This pattern generates a warm flow and may foster photochemical pollution. Our study is the first step toward the establishment of an alert system. It will help to provide recommendations for coping with concurrent heat waves and air pollution episodes.

Assessment of global and regional PM10 CAMSRA data: comparison to observed data in Morocco.

Given the strong impact of air quality on health, environment, and economy, Morocco has implemented an air quality network to assess air pollutants including PM10 (particulate matter with a diameter less than 10 µm). This network which is composed of 29 fixed measurement stations is spatially limited and does not provide sufficient time resolution. The scarcity of measured air quality data led to seek an optimal alternative source to conduct related data-based studies. This represents the primary objective of this paper. PM10 concentrations of global Copernicus Atmosphere Monitoring Service Reanalysis (CAMSRA) data (4D Variational analysis "4v" and analysis "an"), as well as regional CAMSRA data, were examined against the average daily PM10 concentrations collected from six fixed Moroccan air quality measurement stations in 2016 (i.e., observation data). The verification is carried out by studying and analyzing seasonal, extreme, and annual values. The study shows a strong seasonal dependence with a positive bias in winter and a negative bias during summer. For the study of extreme values, global CAMSRA "an" and "4v" data record significant bias of approximately 184 and 161 µg/m3, respectively. However, the annual analysis shows that the CAMSRA global "an" data have the smallest average bias (20.008 µg/m3) and hence has the closest representation of observation data. We conclude that the CAMSRA global analysis data could be used to compute climatology, study trends, evaluate models, benchmark other reanalysis, or serve as boundary conditions for regional models for past periods.