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Precipitation Gravimetry

Precipitation Gravimetry

Precipitation gravimetry is based on converting an analyte into a sparingly soluble precipitate, which is separated by filtration and weighed. An ideal precipitate should be pure, insoluble, of known composition, and easily filtered from the reaction mixture.
In determining nickel by gravimetric analysis, a precipitant of ethanolic dimethylglyoxime is added to a hot nickel salt solution. This is quickly followed by the dropwise addition of dilute ammonia solution until precipitation occurs. A...

Precipitation Titration: Endpoint Detection Methods

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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.
In the Volhard method, a standard excess of AgNO3 is first added to the...

Precipitation Processes

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The experimental conditions in a gravimetric analysis should be optimized to maximize the particle size and purity of the obtained precipitate. Ideally, the concentration of the precipitating reagent should be low with effective stirring to maintain low relative supersaturation for the growth of large crystals. In homogeneous precipitation, the precipitant is slowly generated by a chemical reaction in the solution to avoid local reagent excesses. For example, urea decomposes gradually to...

Methods of Obtaining Topography

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Topography involves measuring and mapping land elevations, natural features, and artificial structures to create accurate representations of the terrain. Topographic surveying relies on traditional and modern methods, each with distinct advantages and limitations.Traditional Surveying Methods:Transit stadia surveys and plane table surveys were widely used traditional surveying methods. These techniques relied on instruments like theodolites and stadia rods for measuring distances and angles,...

Precipitation and Co-precipitation

Precipitation and Co-precipitation

Precipitation and coprecipitation methods can be used to separate a mixture of ions in a solution. In qualitative inorganic analysis, ions that form sparingly soluble precipitates with the same reagent are separated based on the differences in solubility products. For example, consider the separation of Cu(II) and Fe(II) ions by precipitation as insoluble sulfides. First, copper(II) sulfide is precipitated by the addition of acidic H2S, where the dissociation of H2S is suppressed. Adding H2S...

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Related Experiment Video

Updated: Jul 17, 2025

Measurement of Aerosols Optical Thickness of the Atmosphere using the GLOBE Handheld Sun Photometer

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Graph Machine Learning for Improved Imputation of Missing Tropospheric Ozone Data.

Clara Betancourt¹, Cathy W Y Li^1,2, Felix Kleinert¹

¹Jülich Supercomputing Centre, Forschungszentrum Jülich, 52425 Jülich, Germany.

Environmental Science & Technology

|September 4, 2023

Summary

Addressing missing atmospheric pollutant data is crucial for accurate trend analysis. This study introduces a hybrid graph machine learning approach to effectively impute gaps in tropospheric ozone measurements, improving data reliability.

Keywords:

air quality graph machine learning graph signal processing missing data imputation tropospheric ozone

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Area of Science:

Environmental Science
Data Science
Atmospheric Chemistry

Background:

Gaps in atmospheric pollutant measurements hinder impact and trend assessments.
Accurate data imputation is essential for reliable environmental monitoring.

Purpose of the Study:

To develop and evaluate a novel method for imputing missing tropospheric ozone data.
To enhance the accuracy of ozone data imputation using graph machine learning.

Main Methods:

Utilized the graph machine learning algorithm "correct and smooth" for data imputation.
Applied auxiliary data characterizing measurement locations and neighboring ozone observations.
Compared performance across different gap patterns: short (hours), long (months), and multi-station gaps.

Main Results:

Linear interpolation proved most accurate for short gaps (up to 5 hours).
A random forest combined with "correct and smooth" effectively imputed longer gaps at single stations.
The "correct and smooth" algorithm improved random forest performance for multi-station gaps, even with limited neighborhood data.

Conclusions:

A hybrid approach combining linear interpolation and graph machine learning is recommended for tropospheric ozone time series imputation.
The proposed method enhances the reliability of atmospheric pollutant data analysis.
Graph machine learning offers a powerful tool for addressing data gaps in environmental monitoring networks.