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Related Concept Videos

Precipitation Processes01:12

Precipitation Processes

5.3K
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...
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Precipitation and Co-precipitation01:17

Precipitation and Co-precipitation

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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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Precipitation Gravimetry01:03

Precipitation Gravimetry

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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...
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Precipitation Titration: Overview01:26

Precipitation Titration: Overview

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Precipitation titration involves the reaction of a titrant and an analyte to generate an insoluble precipitate. While precipitation titration uses various precipitating agents, silver nitrate is the most common precipitating reagent; titrations involving Ag+ are called argentometric titrations. Usually, the endpoint in a precipitation titration can be detected by visual indicators.
A precipitation titration curve demonstrates the change in concentration of the titrant or analyte upon adding the...
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Influence of Earth's Curvature and Atmospheric Refraction on Leveling01:26

Influence of Earth's Curvature and Atmospheric Refraction on Leveling

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During leveling, the Earth's curvature and atmospheric refraction introduce deviations in the line of sight from a true horizontal reference. When the line of sight is leveled, it remains perpendicular to the plumb line only at a single point. Beyond this, it deviates due to the Earth’s curvature, represented by the correction C. For a sight distance D, the deviation can be derived using the relationship:This relationship shows that the deviation increases quadratically with distance. Over a...
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Related Experiment Video

Updated: Jan 17, 2026

Surface Renewal: An Advanced Micrometeorological Method for Measuring and Processing Field-Scale Energy Flux Density Data
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Preparing Dispersion Model Surface Meteorological Inputs Using High-Resolution Rapid Refresh (HRRR) Data.

Xueying Zhang1,2, Elaine Symanski1,2, Hannah Renee Paduch2

  • 1Department of Medicine, Section of Epidemiology and Population Sciences, Baylor College of Medicine, Houston, TX, USA.

Research Square
|September 18, 2025
PubMed
Summary
This summary is machine-generated.

High-Resolution Rapid Refresh (HRRR) data improves air pollution dispersion modeling by providing more accurate meteorological inputs than traditional weather stations. This novel framework enhances predictions, especially in areas with sparse observational data.

Keywords:
Dispersion modelHRRRair pollutionmeteorologic data

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Area of Science:

  • Atmospheric Science
  • Environmental Science
  • Air Quality Modeling

Background:

  • Accurate meteorological data is crucial for air pollution dispersion modeling.
  • Traditional models use fixed-location weather station data, which has limitations in capturing fine-scale variability due to sparse distribution.
  • Gaps in observational data hinder accurate pollution predictions, particularly in remote or data-sparse regions.

Purpose of the Study:

  • To develop and evaluate a novel framework for generating American Meteorological Society/Environmental Protection Agency (EPA) Regulatory Model (AERMOD) compatible surface meteorology data using the High-Resolution Rapid Refresh (HRRR) dataset.
  • To assess the performance of HRRR-derived meteorology data in predicting traffic-related nitrogen dioxide (NO2) concentrations using the Research LINE source (R-LINE) dispersion model.
  • To compare the predictive accuracy of HRRR data against traditional observational meteorological data.

Main Methods:

  • Generated AERMOD-compatible surface meteorology (.sfc) data from the 3-km, hourly HRRR dataset.
  • Created three scenarios for HRRR meteorology data, including adjustments for convective and stable planetary boundary layer conditions.
  • Applied HRRR-derived and AERMET-processed observational meteorology data in the R-LINE model to predict NO2 concentrations at 443 U.S. monitoring sites.
  • Evaluated model performance using coefficient of determination (R2) and Index of Agreement (IOA) by comparing predicted NO2 with measured data.

Main Results:

  • HRRR-derived meteorology data scenarios yielded a higher average R2 (0.26) compared to observational data (R2=0.16), indicating over a 60% increase in explained variance.
  • HRRR data generally outperformed observational data in predicting NO2 concentrations, especially in areas farther from weather stations and with varying traffic magnitudes.
  • Site-specific Index of Agreement analyses showed HRRR inputs performed better across most of the continental U.S., though less effectively in dense urban areas compared to observational data.

Conclusions:

  • The High-Resolution Rapid Refresh (HRRR) dataset offers a viable and potentially superior alternative to traditional observational data for air pollution dispersion modeling.
  • The developed framework demonstrates the potential of HRRR data to improve the accuracy of dispersion models, particularly in data-scarce environments.
  • HRRR data shows enhanced predictive capabilities for pollutants like nitrogen dioxide, highlighting its utility for air quality management and research.