# CWA 303(d) Impaired Waters Proximity Screener — NPDES/Phase-I (`malonestar/epa-impaired-waters-303d-screener`) Actor CWA Section 303(d) impaired-waters proximity API — screens coordinates against EPA ATTAINS impaired/threatened waters: nearest impaired stream or lake, distance, pollutant causes, TMDL status, and an NPDES/stormwater permitting flag for Phase-I ESA site due diligence. - **URL**: https://apify.com/malonestar/epa-impaired-waters-303d-screener.md - **Developed by:** [Kyle Maloney](https://apify.com/malonestar) (community) - **Categories:** Real estate, Developer tools, Agents - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $4.40 / 1,000 results This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events. Since this Actor supports Apify Store discounts, the price gets lower the higher subscription plan you have. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event ## What's an Apify Actor? Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases. In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours, and optionally produces a well-defined JSON output, datasets with results, or files in key-value store. In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server. Actors are written with capital "A". ## How to integrate an Actor? If asked about integration, you help developers integrate Actors into their projects. You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready. The best way to integrate Actors is as follows. In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js/docs.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python/docs.md): ```bash pip install apify-client ``` In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md): ````bash # MacOS / Linux curl -fsSL https://apify.com/install-cli.sh | bash # Windows irm https://apify.com/install-cli.ps1 | iex ```bash In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/integrations/mcp.md). If your project is in a different language, use the [REST API](https://docs.apify.com/api/v2.md). For usage examples, see the [API](#api) section below. For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt). # README ## EPA Impaired Waters (CWA 303d) Proximity Screener API Screen any set of coordinates against the EPA's authoritative **Clean Water Act Section 303(d)** impaired/threatened-waters list (ATTAINS) — the same list every NPDES discharge permit, stormwater application, and Phase-I water-resources review has to check. For each site you get: is it in or near an impaired stream, lake, or estuary; how far; what pollutants caused the impairment; whether a TMDL (cleanup plan) exists; and a one-line Phase-I-style recommendation. ### Who this is for - **Environmental consultants** running Phase-I/Phase-II ESAs who need the CWA §303(d) water-resources line item without manually querying EPA's ATTAINS map for every parcel. - **NPDES / stormwater permit applicants** (industrial, construction, MS4) who need to know up front whether their discharge point is near a water that will trigger antidegradation review or tighter effluent limits. - **Energy and industrial site-selection teams** (solar, wind, data centers, manufacturing) screening candidate sites for hidden permitting cost/schedule risk before committing to a location. - **Land developers, CRE lenders, and title/due-diligence teams** who want a fast, repeatable water-quality-risk check across a portfolio of parcels. - **AI agents** (Claude, Cursor, and other MCP clients) doing automated site due diligence — clean, typed JSON output with field-level descriptions makes this a reliable chainable tool alongside our other federal-overlay screeners (wetlands, critical habitat, flood risk, karst, sole-source aquifers). ### What it does For every coordinate you provide, the actor queries the EPA/Esri Living Atlas **ATTAINS** FeatureServer — the authoritative mirror of EPA's impaired/threatened-waters and TMDL data — across all three of its geometry layers (assessed points, streams/rivers, and lakes/reservoirs/estuaries) within your search radius, then: - Flags whether the site sits **inside** a mapped impaired-water polygon, or is simply **near** one. - Reports the **nearest** impaired/threatened water's name, type, distance, and CWA §303(d) listing status. - Returns a **de-duplicated list of confirmed impairment causes** (e.g. Pathogens, Nutrients, Mercury, PFAS, Sediment) across every match found within the radius. - Derives a plain-language **TMDL status** (TMDL Established / On 303(d) List — TMDL Pending / Alternative Plan in Place / Not Assessed). - Sets an editorial **NPDES/stormwater permitting flag** and a one-line **Phase-I recommendation** you can drop straight into a report. ### Example input ```json { "assets": [ { "lat": 39.1463728, "lon": -77.0356535, "label": "Proposed outfall — NW Branch Anacostia, MD" }, { "lat": 39.5296, "lon": -117.0372, "label": "Rural Nevada parcel" } ], "radiusMiles": 1, "maxAssets": 200 } ```` `assets[]` is the only required input — each item needs `lat`/`lon` (WGS84 decimal degrees) and an optional `label`. `radiusMiles` defaults to 1 (a typical Phase-I / NPDES-permitting screening distance) and is clamped to 0.1–25 miles. ### Output fields (one row per asset) | Field | Description | |---|---| | `asset_label`, `asset_lat`, `asset_lon` | Echoes the screened coordinate. | | `in_impaired_water` | True if the coordinate falls inside a mapped impaired-water polygon (lake/reservoir/estuary). | | `near_impaired_water` | True if any impaired/threatened water was found within `radius_miles`. | | `impaired_waters_within_radius` | Count of distinct matched assessment units. | | `nearest_waterbody_name`, `nearest_geometry_type`, `nearest_state` | Identity of the nearest match. | | `nearest_distance_miles`, `nearest_distance_km` | Straight-line proximity to the nearest match (0 = inside/on it). | | `nearest_on_303d_list`, `nearest_tmdl_status`, `nearest_overall_status` | CWA §303(d) listing and cleanup-plan status. | | `impairment_causes` | De-duplicated confirmed pollutant causes across all matches within radius. | | `npdes_stormwater_flag` | Editorial screening flag for discharge/stormwater permit difficulty. | | `phase1_recommendation` | One-line, editorial Phase-I-style summary and next step. | | `nearest_waterbody_report_link` | Link to EPA's How's My Waterway detail report. | | `error` | Populated only if this specific asset could not be screened; other assets in the same run are unaffected. | ### Use as an MCP tool This actor is Pay-Per-Event priced and discoverable via `mcp.apify.com` — point an MCP-compatible agent (Claude, Cursor, etc.) at it and call it directly as a due-diligence tool, chaining it with the portfolio's other proximity screeners (wetlands, critical habitat, karst, floodplain, sole-source aquifers) for a full site-constraints sweep from a single coordinate. ### FAQ **Is this the official EPA data?** Yes — it queries the Esri Living Atlas mirror of EPA's ATTAINS Clean Water Act Section 303(d) impaired-waters and TMDL dataset, refreshed roughly quarterly. **Does "near" mean the site actually drains to that water?** No. `nearest_distance_miles` is a straight-line proximity proxy, not a hydrologic drainage delineation. Confirm actual receiving-water drainage with a site-specific study before any permitting decision — see the `disclaimer` field on every row. **What counts as an "impairment cause"?** Only pollutants EPA ATTAINS classifies as a confirmed `"Cause"` for that water (as opposed to "Insufficient Information" or "Observed Effect", which are excluded from the causes list but visible if you inspect the raw service). **How many sites can I screen at once?** Up to `maxAssets` (default 200, max 2000) per run; each asset is billed as one Result event. **What if the EPA service is temporarily renamed or down?** The actor resolves the current service URL from a stable ArcGIS item id at runtime, so routine quarterly renames need no code change. If EPA/Esri's service is genuinely unavailable, affected assets return a clear `error` row instead of failing the whole run. # Actor input Schema ## `assets` (type: `array`): Site coordinates to screen against EPA CWA Section 303(d) impaired/threatened waters. Each item: { lat, lon, label? }. lat/lon are decimal degrees (WGS84); label is an optional free-text identifier echoed back on the output row (defaults to asset\_1, asset\_2, ...). One billable result row is returned per asset, even when the site is clear. ## `radiusMiles` (type: `number`): How far from each asset coordinate to search for CWA Section 303(d) impaired or threatened waterbodies, streams, and lakes. Minimum 0.1, maximum 25. Default 1 mile — a typical Phase-I / NPDES-permitting screening distance. ## `maxAssets` (type: `integer`): Safety cap on how many assets from the assets array will be screened in a single run (extra assets are skipped with a warning, not an error). ## Actor input object example ```json { "assets": [ { "lat": 39.1463728, "lon": -77.0356535, "label": "Northwest Branch Anacostia River, MD (on a mapped impaired stream segment)" }, { "lat": 39.5296, "lon": -117.0372, "label": "Rural Nevada desert site (expected clear)" } ], "radiusMiles": 1, "maxAssets": 200 } ``` # Actor output Schema ## `results` (type: `string`): No description # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = { "assets": [ { "lat": 39.1463728, "lon": -77.0356535, "label": "Northwest Branch Anacostia River, MD (on a mapped impaired stream segment)" }, { "lat": 39.5296, "lon": -117.0372, "label": "Rural Nevada desert site (expected clear)" } ], "radiusMiles": 1, "maxAssets": 200 }; // Run the Actor and wait for it to finish const run = await client.actor("malonestar/epa-impaired-waters-303d-screener").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`); const { items } = await client.dataset(run.defaultDatasetId).listItems(); items.forEach((item) => { console.dir(item); }); // 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs ``` ## Python example ```python from apify_client import ApifyClient # Initialize the ApifyClient with your Apify API token # Replace '' with your token. client = ApifyClient("") # Prepare the Actor input run_input = { "assets": [ { "lat": 39.1463728, "lon": -77.0356535, "label": "Northwest Branch Anacostia River, MD (on a mapped impaired stream segment)", }, { "lat": 39.5296, "lon": -117.0372, "label": "Rural Nevada desert site (expected clear)", }, ], "radiusMiles": 1, "maxAssets": 200, } # Run the Actor and wait for it to finish run = client.actor("malonestar/epa-impaired-waters-303d-screener").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "assets": [ { "lat": 39.1463728, "lon": -77.0356535, "label": "Northwest Branch Anacostia River, MD (on a mapped impaired stream segment)" }, { "lat": 39.5296, "lon": -117.0372, "label": "Rural Nevada desert site (expected clear)" } ], "radiusMiles": 1, "maxAssets": 200 }' | apify call malonestar/epa-impaired-waters-303d-screener --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=malonestar/epa-impaired-waters-303d-screener", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "CWA 303(d) Impaired Waters Proximity Screener — NPDES/Phase-I", "description": "CWA Section 303(d) impaired-waters proximity API — screens coordinates against EPA ATTAINS impaired/threatened waters: nearest impaired stream or lake, distance, pollutant causes, TMDL status, and an NPDES/stormwater permitting flag for Phase-I ESA site due diligence.", "version": "1.0", "x-build-id": "EzcbOaCD8yXiXel7y" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/malonestar~epa-impaired-waters-303d-screener/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-malonestar-epa-impaired-waters-303d-screener", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/malonestar~epa-impaired-waters-303d-screener/runs": { "post": { "operationId": "runs-sync-malonestar-epa-impaired-waters-303d-screener", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/malonestar~epa-impaired-waters-303d-screener/run-sync": { "post": { "operationId": "run-sync-malonestar-epa-impaired-waters-303d-screener", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } } }, "components": { "schemas": { "inputSchema": { "type": "object", "required": [ "assets" ], "properties": { "assets": { "title": "Assets (coordinates to screen)", "type": "array", "description": "Site coordinates to screen against EPA CWA Section 303(d) impaired/threatened waters. Each item: { lat, lon, label? }. lat/lon are decimal degrees (WGS84); label is an optional free-text identifier echoed back on the output row (defaults to asset_1, asset_2, ...). One billable result row is returned per asset, even when the site is clear.", "default": [ { "lat": 39.1463728, "lon": -77.0356535, "label": "Northwest Branch Anacostia River, MD (on a mapped impaired stream segment)" }, { "lat": 39.5296, "lon": -117.0372, "label": "Rural Nevada desert site (expected clear)" } ] }, "radiusMiles": { "title": "Search radius (miles)", "minimum": 0.1, "maximum": 25, "type": "number", "description": "How far from each asset coordinate to search for CWA Section 303(d) impaired or threatened waterbodies, streams, and lakes. Minimum 0.1, maximum 25. Default 1 mile — a typical Phase-I / NPDES-permitting screening distance.", "default": 1 }, "maxAssets": { "title": "Max assets per run", "minimum": 1, "maximum": 2000, "type": "integer", "description": "Safety cap on how many assets from the assets array will be screened in a single run (extra assets are skipped with a warning, not an error).", "default": 200 } } }, "runsResponseSchema": { "type": "object", "properties": { "data": { "type": "object", "properties": { "id": { "type": "string" }, "actId": { "type": "string" }, "userId": { "type": "string" }, "startedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "finishedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "status": { "type": "string", "example": "READY" }, "meta": { "type": "object", "properties": { "origin": { "type": "string", "example": "API" }, "userAgent": { "type": "string" } } }, "stats": { "type": "object", "properties": { "inputBodyLen": { "type": "integer", "example": 2000 }, "rebootCount": { "type": "integer", "example": 0 }, "restartCount": { "type": "integer", "example": 0 }, "resurrectCount": { "type": "integer", "example": 0 }, "computeUnits": { "type": "integer", "example": 0 } } }, "options": { "type": "object", "properties": { "build": { "type": "string", "example": "latest" }, "timeoutSecs": { "type": "integer", "example": 300 }, "memoryMbytes": { "type": "integer", "example": 1024 }, "diskMbytes": { "type": "integer", "example": 2048 } } }, "buildId": { "type": "string" }, "defaultKeyValueStoreId": { "type": "string" }, "defaultDatasetId": { "type": "string" }, "defaultRequestQueueId": { "type": "string" }, "buildNumber": { "type": "string", "example": "1.0.0" }, "containerUrl": { "type": "string" }, "usage": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "integer", "example": 1 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } }, "usageTotalUsd": { "type": "number", "example": 0.00005 }, "usageUsd": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "number", "example": 0.00005 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } } } } } } } } } ```