Industrial Water Survey (IWS)
This survey is being conducted to fulfill the requirements for producing national environmental indicators of water quality.
Detailed information for 2009
Data release - March 5, 2012; September 7, 2012 (correction)
The Industrial Water Survey provides information about the quantities of water consumed and costs, sources, treatments and discharge of water used for manufacturing, mining and power generating industries. Additional industries will be surveyed in subsequent years. This survey is being conducted to fulfill the requirements for producing national environmental indicators of water quality.
- Environmental quality
Data sources and methodology
The target population consists of locations primarily engaged in manufacturing, coal mining, metal ore mining, non-metallic mineral mining (excluding sand and gravel quarrying), nuclear electric power generation and fossil-fuel electric power generation.
The Industrial Water Survey uses three separate questionnaires to collect data from respondents. A separate questionnaire was designed for each of the three sectors being surveyed, i.e. one for manufacturing, one for the mineral extraction industries and another for the thermal-electric power generators.
The questionnaires collect data on the volume of water brought into the facility, including information on the source, purpose, treatment and possible re-circulation of this water, by industrial users. As well, data is collected on the volumes of water discharged and treatment of this discharged water by industrial users. Cost information on the intake and discharge of water is also collected.
The questionnaires were developed in collaboration with data users in order to meet their statistical needs. Respondents were also consulted through individual meetings to ensure the information being asked was available and that the questionnaire could be filled out within a reasonable timeframe.
This is a sample survey with a cross-sectional design.
The frame used for sampling purposes is the Statistics Canada Business Register, with the observed population comprised of all manufacturing, selected mining and all thermal-electric locations. The statistical unit is the location. The population size was 103,625 manufacturing locations (NAICS 31 - 33), 696 mining locations (NAICS 2121, 2122, 2123, excl. 21232) and 112 thermal-electric power generating plants (NAICS 221112, 221113).
There is an independent sampling strategy for each of the three sectors. The sample for the thermal-electric power generating stations is a census of the approximately 112 electric power stations. A probability design is used for sample selection in the manufacturing and mineral extraction sectors. In the mining sector, establishments are stratified by province, by 4-digit NAICS industry and by size (revenues). All of the approximately 359 in-sample units receive a questionnaire. In the manufacturing sector, establishments are stratified by major river drainage region, by industry and by size (shipments). To reduce response burden on small units, the smallest units of the industries of interest are excluded from sampling. In each combination of industries, locations that make up the bottom 5% of the size measure by major river basin were excluded. Some specific industries, identified as large consumers of water are selected with certainty; the rest of the population is sampled with varying sampling fractions, depending on the industry. All of the approximately 5024 in-sample units receive a questionnaire.
Data collection for this reference period: April 01, 2010 to February 28, 2011
Responding to this survey is mandatory.
Data are collected directly from survey respondents.
Mail out is usually directed to an "environment manager or coordinator". Respondents are asked to return the completed questionnaires within thirty days of receipt. Fax reminders are sent to respondents whose questionnaires are outstanding 45 days after the mail out.
View the Questionnaire(s) and reporting guide(s).
Many factors affect the accuracy of data produced in a survey. For example, respondents may have made errors in interpreting questions, answers may have been incorrectly entered on the questionnaires, and errors may have been introduced during the data capture or tabulation process. Every effort was made to reduce the occurrence of such errors in the survey.
Returned data are first checked using an automated edit-check program (BLAISE) immediately after capture. This first procedure verifies that all mandatory cells have been filled in, that certain values lie within acceptable ranges, that questionnaire flow patterns have been respected, and that totals equal the sum of their components. Collection officers evaluate the edit failures and concentrate follow-up efforts accordingly.
Further data checking is performed by subject matter officers who compare historical data with returned data to determine if differences between survey cycles are reasonable. If not, collection officers are asked to confirm with respondents their responses. Subject matter officers also research companies (annual reports, web sites, etc.) in an effort to verify information submitted by respondents.
Statistical imputation is used for partial response records. Five methods of imputations were used for the Industrial Water Survey: Deterministic imputation (only one possible value for the field to impute), historical imputation, imputation by ratio, donor imputation (using a "nearest neighbour" approach to find a valid record that is most similar to the record requiring imputation) and manual imputation. Ratios were calculated and donors were selected for imputation purposes based on the same or closest industry group within specified geographic areas.
The response values for sampled units were multiplied by a sampling weight in order to estimate for the entire population. The sampling weight was calculated using a number of factors, including the probability of the unit being selected in the sample. Finally, the weights were adjusted to account for the uncovered portion and for respondents who could not be contacted or who refused to complete the survey.
Data evaluation and error detection during data collection is an important way to ensure that the final estimates that are produced are of good quality. However, the survey results are evaluated after data collection is over and the estimates have been produced. One way to assess data quality is to compare it to the trends of other data collected. For the Industrial Water Survey, estimates of 2009 were compared with the estimates of the 2007 and 2005 editions. This historical comparison was made to make sure that the estimates were coherent.
Statistics Canada is prohibited by law from releasing any information it collects which could identify any person, business, or organization, unless consent has been given by the respondent or as permitted by the Statistics Act. Various confidentiality rules are applied to all data that are released or published to prevent the publication or disclosure of any information deemed confidential. If necessary, data are suppressed to prevent direct or residual disclosure of identifiable data.
Sampling errors arise from the fact that the information obtained from a sample of the population is applied to the entire population. The sampling method as well as the estimation method, the sample size and the variability associated to each measured variable determine the sampling error. A possible measure of sampling errors is the coefficient of variation (CV). It represents the proportion of the estimate that comes from the variability associated to it. For the Industrial Water Survey, CVs were calculated for the major variables and are indicated on the data tables. This information is available in the Statistics Canada publication entitled "Industrial Water Use, 2009" (catalogue number 16-401-X), accessible through the 'Publications' link in the side bar menu at the upper left of this screen (scroll up to view).
Data response error may be due to questionnaire design, the characteristics of a question, inability or unwillingness of the respondent to provide correct information, misinterpretation of the questions or definitional problems. These errors are controlled through careful questionnaire design and testing and the use of simple concepts and consistency checks.
Processing errors may occur at various stages of processing such as data entry, editing and tabulation. Measures have been taken to minimize these errors.
Non-response error results when respondents' refuse to answer, are unable to respond or are too late in reporting. Total non-response, i.e. when all questions from the survey are left unanswered, is dealt with by adjusting the weights assigned to the responding records, such that one responding record might also represent other non-responding units with similar characteristics (i.e. size, province, industry). Missing data items are imputed for partial non-responses (i.e. when only some questions are left unanswered).
The response rate for the manufacturing component of the survey was 70%, for the mining component, 79% and 84% for the thermal-electric component in the 2009 reference year.
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