The data in this section is derived from surveys conducted by visitors to our website. Questions in these surveys are designed to be similar to many scientific and government surveys.
Each entry in the survey is assigned a number within the range of -2 to +2, where -2 represents a strongly negative perception and +2 represents a strongly positive perception.
To ensure data accuracy, we have implemented filtering measures to identify and exclude potential spam from our calculations. Our algorithms identify users who exhibit spam-like behavior and their inputs are not considered in the calculations. This helps maintain the integrity of the data and provide reliable results.
To make survey results easier to interpret for our users, we present them on a scale ranging from 0 to 100. This scale allows for a clear and straightforward understanding of the data, enhancing user experience and facilitating meaningful comparisons.
Our current index, which is continuously updated, is generated using data up to 36 months old. We carefully select cities for inclusion in the index based on a minimum number of contributors to ensure statistical significance. Additionally, our semiannual index is calculated twice a year by incorporating the latest data into the historical view.
Most of our data are based on perceptions (opinions) from visitors of this website. For the pollution section, we include relevant data from World Health Organization and other institutions if we find it helpful. Please consult our Terms of use for details.
Pollution Index provides an estimation of overall pollution levels in cities worldwide. It considers factors such as air and water pollution, garbage disposal, cleanliness, noise and light pollution, green spaces, and comfort in relation to pollution. Numbeo assigns the highest weight to air pollution, followed by water pollution and accessibility, which are considered the two primary pollution factors. Other types of pollution receive a smaller weight in the index.
Pollution Exp Scale uses an exponential function to show very high numbers for very polluted cities and very low numbers for unpolluted cities.
Actual formulas to calculate indices are subject to change. At this moment, quite complex empirical formulas are used. Those formulas, as written in the Java programming language, are as follows:
public void calculateIndex() {
//assumes air_quality and other entries from user are in the range [-2, 2], where -2 means perceived as very low, and +2 means very high
//PollutionDbEntry.IS_POLLUTION_AIR_QUALITY and similar are constant variables which are either -1 and 1; i.e. IS_POLLUTION_AIR_QUALITY = -1.0
//These constant variables in PollutionDbEntry class are 1 for values which represent pollutions and -1 for values which represent opposite (purity, cleanliness)
index = new PollutionIndex();
double overall = 0.0;
overall += 7 * getIndexPartPreCalc(PollutionDbEntry.IS_POLLUTION_AIR_QUALITY * air_quality);
overall += 2 * getIndexPartPreCalc(PollutionDbEntry.IS_POLLUTION_DRINKING_WATER_QUALITY_ACCESSIBILITY * drinking_water_quality_accessibility);
overall += 2 * getIndexPartPreCalc(PollutionDbEntry.IS_POLLUTION_WATER_POLLUTION * water_pollution);
overall += getIndexPartPreCalc(PollutionDbEntry.IS_POLLUTION_GARBAGE_DISPOSAL_SATISFACTION * garbage_disposal_satisfaction);
overall += getIndexPartPreCalc(PollutionDbEntry.IS_POLLUTION_CLEAN_AND_TIDY * clean_and_tidy);
overall += getIndexPartPreCalc(PollutionDbEntry.IS_POLLUTION_NOISE_AND_LIGHT_POLLUTION * noise_and_light_pollution);
overall += getIndexPartPreCalc(PollutionDbEntry.IS_POLLUTION_GREEN_AND_PARKS_QUALITY * green_and_parks_quality);
overall += 2 * getIndexPartPreCalc(PollutionDbEntry.IS_POLLUTION_COMFORTABLE_TO_SPEND_TIME * comfortable_to_spend_time);
double overallExpScale = 0.0;
overallExpScale += 7 * getIndexPartPreCalcExpScaleStandard(PollutionDbEntry.IS_POLLUTION_AIR_QUALITY * air_quality);
overallExpScale += 2 * getIndexPartPreCalcExpScaleStandard(PollutionDbEntry.IS_POLLUTION_DRINKING_WATER_QUALITY_ACCESSIBILITY * drinking_water_quality_accessibility);
overallExpScale += 2 * getIndexPartPreCalcExpScaleStandard(PollutionDbEntry.IS_POLLUTION_WATER_POLLUTION * water_pollution);
overallExpScale += getIndexPartPreCalcExpScaleStandard(PollutionDbEntry.IS_POLLUTION_GARBAGE_DISPOSAL_SATISFACTION * garbage_disposal_satisfaction);
overallExpScale += getIndexPartPreCalcExpScaleStandard(PollutionDbEntry.IS_POLLUTION_CLEAN_AND_TIDY * clean_and_tidy);
overallExpScale += getIndexPartPreCalcExpScaleStandard(PollutionDbEntry.IS_POLLUTION_NOISE_AND_LIGHT_POLLUTION * noise_and_light_pollution);
overallExpScale += getIndexPartPreCalcExpScaleStandard(PollutionDbEntry.IS_POLLUTION_GREEN_AND_PARKS_QUALITY * green_and_parks_quality);
overallExpScale += 2 * getIndexPartPreCalcExpScaleStandard(PollutionDbEntry.IS_POLLUTION_COMFORTABLE_TO_SPEND_TIME * comfortable_to_spend_time);
index.main = overall / 14.5; //max 17
index.expScale = calcScaleStandardIndexFromSum(overallExpScale, 12);
}
protected double getIndexPartPreCalc(double internalValue) {
return (internalValue + 2) * 25;
}
protected double getIndexPartPreCalcExpScaleStandard(double internalValue) {
return getIndexPartPreCalcExpScale(internalValue, Math.E);
}
protected double getIndexPartPreCalcExpScale(double internalValue, double exp) {
return Math.pow((internalValue + 2) * 25, exp);
}
protected double calcScaleStandardIndexFromSum(double scaleSum, int elems) {
return Math.pow(scaleSum / elems, 1 / (Math.E * 8.8 / 10));
}