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Precipitation Titration: Endpoint Detection Methods

Precipitation Titration: Endpoint Detection Methods

In argentometric precipitation titrations, endpoints can be detected visually by the Mohr, Volhard, and Fajans methods. In the Mohr method, adding a soluble chromate indicator gives an initial yellow color to the analyte solution. As the titrant is added, the first excess of silver ions forms a red silver chromate precipitate, marking the endpoint. The solution pH should be maintained at about 8 by adding solid CaCO3.
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Related Experiment Video

Updated: Jul 13, 2026

Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling SAHM

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Published on: October 11, 2016

Predicting particulate matter ( ) levels in Morocco: a 5-day forecast using the analog ensemble method.

Anass Houdou^1,2, Kenza Khomsi³, Luca Delle Monache⁴

¹International School of Public Health, Mohammed VI University of Sciences and Health, Casablanca, Morocco. ahoudou@um6ss.ma.

Environmental Monitoring and Assessment

|December 2, 2024

Summary

Accurate 5-day particulate matter (PM) forecasts for Morocco were achieved using analog ensemble (AnEn) and bias correction (AnEnBc) techniques. This method significantly improved forecast accuracy, aiding public health and environmental planning.

Keywords:

Air pollution Analog ensemble model Copernicus Atmosphere Monitoring Service Forecasting Morocco Particulate matter

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Area of Science:

Environmental Science
Atmospheric Science
Public Health

Background:

Accurate prediction of particulate matter (PM) levels is vital for public health and environmental planning, especially concerning natural pollutants like dust storms.
Existing forecasts require enhancement for improved accuracy in specific regions.

Purpose of the Study:

To develop accurate 5-day particulate matter (PM) forecasts for Morocco.
To evaluate the effectiveness of analog ensemble (AnEn) and bias correction (AnEnBc) techniques in post-processing atmospheric composition forecasts.

Main Methods:

Post-processing of Copernicus Atmosphere Monitoring Service (CAMS) global forecasts using analog ensemble (AnEn) and bias correction (AnEnBc) techniques.
Utilizing CAMS reanalysis data as a reference for model training and validation.

Main Results:

Significant reduction in Root Mean Squared Error (RMSE) from 63.83 to 44.73 µg/m³.
Mean Absolute Error (MAE) decreased from 36.70 to 24.30 µg/m³.
Coefficient of determination (R²) increased from 29.11% to 65.18%, and Pearson correlation coefficient improved from 0.61 to 0.82.

Conclusions:

The integration of reanalysis data and AnEn techniques substantially improved PM 5-day forecasting accuracy in Morocco.
This study represents the first application of this advanced forecasting approach in Morocco and the MENA region.
The methodology holds potential for early and accurate warnings of PM pollution events, informing environmental policies and public health decisions.