Quantitative risk assessment studies aim to evaluate the health impact of environmental factors, policies, or exposures by quantifying the number of disease cases or disability-adjusted life-years (DALYs) attributable to them. These studies involve defining counterfactual scenarios, assessing exposure, quantifying risks using dose-response functions, and analyzing uncertainties. Key challenges include accurately assessing factors at different geographic scales, accounting for correlations and synergies between factors, and improving the handling of emerging factors with limited data. The methodology also requires clarification of terminology, such as health impact assessment (HIA) and risk assessment, which are often similar in practice. The main steps of quantitative risk assessment include identifying factors, defining the study area and population, describing counterfactual scenarios, assessing exposure, identifying health outcomes and dose-response functions, quantifying baseline disease frequency or DALYs, estimating health risk, assessing social and economic impacts, and performing uncertainty analyses. These steps are often followed by economic assessments to translate health impacts into economic costs. Quantitative risk assessment studies are used to evaluate the impact of policies or interventions, such as reducing air pollution or improving public health through infrastructure changes. They can also assess the health impact of environmental factors like particulate matter, temperature, or chemical exposures. These studies often rely on environmental models, biomarkers, or questionnaires to estimate exposure levels and health outcomes. However, challenges remain in accurately representing exposure metrics, especially when using questionnaires or environmental models that may not capture individual behaviors or spatial variations. Environmental models, such as dispersion models for air pollution, can provide detailed spatial and temporal estimates of exposure levels, but their resolution and accuracy vary. Combining environmental models with population density data can improve the estimation of population exposure by weighting concentrations according to population distribution. However, models with very fine spatial resolution may not be suitable if individual time-space activity data are unavailable. Overall, quantitative risk assessment studies are essential for informing policy decisions by providing a structured approach to evaluating the health impact of environmental factors and interventions. They require careful consideration of exposure metrics, dose-response functions, and uncertainties to ensure accurate and relevant results.Quantitative risk assessment studies aim to evaluate the health impact of environmental factors, policies, or exposures by quantifying the number of disease cases or disability-adjusted life-years (DALYs) attributable to them. These studies involve defining counterfactual scenarios, assessing exposure, quantifying risks using dose-response functions, and analyzing uncertainties. Key challenges include accurately assessing factors at different geographic scales, accounting for correlations and synergies between factors, and improving the handling of emerging factors with limited data. The methodology also requires clarification of terminology, such as health impact assessment (HIA) and risk assessment, which are often similar in practice. The main steps of quantitative risk assessment include identifying factors, defining the study area and population, describing counterfactual scenarios, assessing exposure, identifying health outcomes and dose-response functions, quantifying baseline disease frequency or DALYs, estimating health risk, assessing social and economic impacts, and performing uncertainty analyses. These steps are often followed by economic assessments to translate health impacts into economic costs. Quantitative risk assessment studies are used to evaluate the impact of policies or interventions, such as reducing air pollution or improving public health through infrastructure changes. They can also assess the health impact of environmental factors like particulate matter, temperature, or chemical exposures. These studies often rely on environmental models, biomarkers, or questionnaires to estimate exposure levels and health outcomes. However, challenges remain in accurately representing exposure metrics, especially when using questionnaires or environmental models that may not capture individual behaviors or spatial variations. Environmental models, such as dispersion models for air pollution, can provide detailed spatial and temporal estimates of exposure levels, but their resolution and accuracy vary. Combining environmental models with population density data can improve the estimation of population exposure by weighting concentrations according to population distribution. However, models with very fine spatial resolution may not be suitable if individual time-space activity data are unavailable. Overall, quantitative risk assessment studies are essential for informing policy decisions by providing a structured approach to evaluating the health impact of environmental factors and interventions. They require careful consideration of exposure metrics, dose-response functions, and uncertainties to ensure accurate and relevant results.