Hydrocarbon processors, particularly in the oil and gas industry, chemical and steel manufacturers, utilities, and semiconductor producers have benefited from emission and effluent services offered by New Millennium Nuclear Technologies (NMNT) personnel. Situations have been carefully assessed, items of equipment and complete systems have been designed, installed, and commissioned, with appropriate operating permit acquisition, and Boiler Incinerator & Furnace (BIF) permitting support provided.

NMNT personnel have the capability to observe hazardous waste trial burns for regulatory authorities. These include:

  • Trial Burn Plans & Quality Assurance Project Plans for Incinerators & BIFs
  • Quality Assurance Management at RCRA Incinerator & BIF Trial Burns
  • Writing Quality Assurance Reports for RCRA Incinerator & BIF Trial Burns
  • TSCA Incinerator, RCRA Incinerator & BIF Trial Burn Sampling and Analysis Procedures
  • Stack Sampling & Trial Burn Sampling and Analysis Procedures
  • Air Permitting (PSD, NSR & Operating Permits for Title V of CAA)
  • Air Pollution Inspections & Environmental Audits (for Air & RCRA)
  • CEM Monitoring plans (Title IV of new CAA) & CEM QA/QC Plans
  • Ambient Air Monitoring Plans & Air Dispersion Modeling Procedures
  • Hazardous Waste Incinerator & Boiler/Industrial Furnace Permitting
  • Workshops in Incinerator & BIF Permitting, sampling & monitoring


In all hazardous site investigations, it is essential to know the quality of the data used for decision-making purposes. The process of generating data of known quality begins in the planning stages when the data quality objectives (DQOs) are established, continued during sample collection activities and laboratory analysis, re-evaluated when validating the analytical data and is finalized as part of the data quality assessment process.

Data Validation includes assessment of the whole raw data package from the laboratory and preparation of the Data Validation Report. The criteria used for evaluating the data are those outlined in the analytical methods, the QA/QC Objectives presented in the “Quality Assurance Project Plan”, USEPA Contract Laboratory Program National Functional Guidelines for Organic Data Review (EPA 540/R-99/008, October 1999), USEPA Contract Laboratory Program National Functional Guidelines for Inorganic Data Review (EPA 540-R-01-008 July 2002 and National Functional Guidelines for Chlorinated Dioxins/Furan Data Review, USEPA Analytical Operations/Data Quality Center (AOC), EPA 540-R-02-003 August 2002.


The importance and the purpose of data validation is to identify "analytical error" and to make final determinations about the overall usability of the data for a project and to ensure that the measurement system (field and laboratory) meets the users’ needs.

In order to perform data validation, certain quality control (QC) checks and analytical procedures must be performed in association with the analysis of the samples. Examination of the results of these checks and procedures allows the trained validator to determine the analytical quality of the data in question. To provide data of known quality, the data validator should:

  1. review the data package to ensure that it contains all the required documents and forms,

  2. assess the results of all QC checks and procedures, and

  3. examine the raw data in detail to verify the accuracy of all information presented by the laboratory.

Data validation focuses on the ability to use the data as intended to make decisions and to address project objectives Therefore, data validation centers on evaluating the extent to which the collected data provide the necessary information to meet the needs of the project’s stakeholders. Data validation requires that appropriate quality assurance and quality control (QA/QC) procedures be followed, and that adequate documentation is included for all data generated both in the laboratory and field. The data is reviewed and any QA/QC criteria not met are “flagged” with qualifiers. The data validation report is prepared based on the findings.

The QA/QC documentation provided by any laboratory, in conjunction with the sample results, allows for the evaluation of the following indicators of data quality:

  1. Integrity and stability of the samples,
  2. Instrument performance during sample analysis,
  3. Possibility of sample contamination,
  4. Identification and quantitation of analytes,
  5. Breakthrough (applicable to VOST).
  6. Analytical precision; and
  7. Analytical accuracy.
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